Patent Publication Number: US-2023132432-A1

Title: Device enhancements for software defined silicon implementations

Description:
RELATED APPLICATION(S) 
     This patent arises from a continuation of U.S. patent application Ser. No. 17/033,267, which is titled “DEVICE ENHANCEMENTS FOR SOFTWARE DEFINED SILICON IMPLEMENTATIONS,” and which was filed on Sep. 25, 2020, which claims the benefit of U.S. Provisional Application Ser. No. 62/907,353, which is titled “SOFTWARE DEFINED SILICON IMPLEMENTATION AND MANAGEMENT,” and which was filed on Sep. 27, 2019. U.S. patent application Ser. No. 17/033,267 also claims the benefit of U.S. Provisional Application Ser. No. 62/937,032, which is titled “SOFTWARE DEFINED SILICON IMPLEMENTATION AND MANAGEMENT,” and which was filed on Nov. 18, 2019. U.S. patent application Ser. No. 17/033,267 further claims the benefit of U.S. Provisional Application Ser. No. 63/049,017, which is titled “SYSTEMS, METHODS, AND APPARATUS FOR SOFTWARE DEFINED SILICON SECURITY,” and which was filed on Jul. 7, 2020. Priority to U.S. patent application Ser. No. 17/033,267, U.S. Provisional Application Ser. No. 62/907,353, U.S. Provisional Application Ser. No. 62/937,032, and U.S. Provisional Application Ser. No. 63/049,017 is claimed. U.S. patent application Ser. No. 17/033,267, U.S. Provisional Application Ser. No. 62/907,353, U.S. Provisional Application Ser. No. 62/937,032, and U.S. Provisional Application Ser. No. 63/049,017 are hereby incorporated by reference herein in their respective entireties. 
    
    
     FIELD OF THE DISCLOSURE 
     This disclosure relates generally to semiconductor devices and, more particularly, to systems, methods, and apparatus for software defined silicon security. 
     BACKGROUND 
     In today&#39;s marketplace, semiconductor device manufacturers ship semiconductor devices, such as microprocessors, with hardware and firmware features fixed, or locked, at the factory. Even if additional, dormant hardware and/or firmware features are included in the shipped semiconductor devices, such dormant features are unable to be activated after the semiconductor devices leave the factory. To gain access to one or more of those dormant features, a customer would need to order, and a manufacturer would need to ship, new versions of the semiconductor devices that have the desired dormant feature(s) activated at the factory. To further complicate matters, the manufacturer may need to predefine and manage numerous different stock keeping units (SKUs) to track the various combinations of different features that are able to be activated on its semiconductor devices, even if some of those combinations are never realized. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG.  1    is a block diagram of an example system to implement and manage software defined silicon products in accordance with teachings of this disclosure. 
         FIG.  2    is a block diagram illustrating example implementations of an example software defined silicon agent, an example manufacturer enterprise system and an example customer enterprise system included in the example system of  FIG.  1   . 
         FIG.  3    illustrates an example software defined silicon management lifecycle implemented by the example systems of  FIGS.  1  and/or  2   . 
         FIG.  4    illustrates example certificates utilized in the example systems of  FIGS.  1  and/or  2    to implement the example lifecycle of  FIG.  4   . 
         FIG.  5    illustrates an example process flow performed by the example systems of  FIGS.  1  and/or  2    to enable initial feature activation in an example software defined silicon product. 
         FIG.  6    illustrates an example process flow performed by the example systems of  FIGS.  1  and/or  2    to enable additional feature activation in an example software defined silicon product. 
         FIG.  7    illustrates an example process flow performed by the example systems of  FIGS.  1  and/or  2    to enable feature deactivation in an example software defined silicon product. 
         FIG.  8    illustrates an example process flow performed by the example systems of  FIGS.  1  and/or  2    to provide customer-initiated feature usage status and billing reconciliation. 
         FIG.  9    illustrates an example process flow performed by the example systems of  FIGS.  1  and/or  2    to provide manufacturer-initiated feature usage status and billing reconciliation. 
         FIG.  10    is a block diagram of another example system to implement and manage software defined silicon products in accordance with teachings of this disclosure. 
         FIG.  11    is a block diagram illustrating example implementations of another example software defined silicon agent, another example manufacturer enterprise system, and another example customer enterprise system included in the example system of  FIG.  10   . 
         FIG.  12    is a block diagram illustrating example implementations of another example software defined silicon agent, another example manufacturer enterprise system, and another example customer enterprise system included in the example system of  FIG.  10   . 
         FIG.  13    is a block diagram of another example system to implement and manage software defined silicon products in accordance with the teachings of this disclosure. 
         FIG.  14    is a block diagram of the example time calculator of  FIG.  13   . 
         FIG.  15    is a block diagram of the example feature group calculator of  FIG.  13   . 
         FIG.  16    is a flowchart representative of example computer readable instructions that may be executed to implement the example manufacturer enterprise system of  FIGS.  1  and/or  2   . 
         FIG.  17    is a flowchart representative of example computer readable instructions that may be executed to implement the example customer enterprise system of  FIGS.  1  and/or  2   . 
         FIG.  18    is a flowchart representative of example computer readable instructions that may be executed to implement the example software defined silicon agent of  FIGS.  1  and/or  2   . 
         FIG.  19    is a flowchart representative of example computer readable instructions that may be executed to implement the example software defined silicon agent of  FIGS.  10  and/or  11   . 
         FIG.  20    is a flowchart representative of example computer readable instructions that may be executed to implement the example software defined silicon agent of  FIGS.  10  and/or  11   . 
         FIG.  21    is a flowchart representative of example computer readable instructions that may be executed to implement the example software defined silicon agent of  FIGS.  10  and/or  11   . 
         FIG.  22    is a flowchart representative of example computer readable instructions that may be executed to implement the example software defined silicon agent of  FIGS.  10  and/or  11   . 
         FIG.  23    is a flowchart representative of example computer readable instructions that may be executed to implement the example software defined silicon agent of  FIGS.  10  and/or  12   . 
         FIG.  24    is a flowchart representative of example computer readable instructions that may be executed to implement the example software defined silicon agent of  FIGS.  10  and/or  12   . 
         FIG.  25    is a flowchart representative of example computer readable instructions that may be executed to implement the example software defined silicon agent of  FIGS.  10 ,  11   , and/or  12 . 
         FIG.  26    is a flowchart representative of example computer readable instructions that may be executed to implement the example time calculator of  FIG.  14   . 
         FIG.  27    is a flowchart representative of example computer readable instructions that may be executed to implement the example feature group calculator of  FIG.  15   . 
         FIG.  28    is a block diagram of an example processor platform structured to execute the example computer readable instructions of  FIG.  16    to implement the example manufacturer enterprise system of  FIGS.  1  and/or  2   . 
         FIG.  29    is a block diagram of an example processor platform structured to execute the example computer readable instructions of  FIG.  17    to implement the example customer enterprise system of  FIGS.  1  and/or  2   . 
         FIG.  30    is a block diagram of an example processor platform structured to execute the example computer readable instructions of  FIG.  18    to implement the example software defined silicon agent of  FIGS.  1  and/or  2   . 
         FIG.  31    is a block diagram of an example processor platform structured to execute the example computer readable instructions of  FIGS.  19 ,  20 ,  21 ,  22   , and/or  25  to implement the example software defined silicon agent of  FIGS.  10 ,  11   , and/or  12 . 
         FIG.  32    is a block diagram of an example processor platform structured to execute the example computer readable instructions of  FIGS.  23 ,  24   , and/or  25  to implement the example software defined silicon agent of  FIGS.  10 ,  11   , and/or  12 . 
         FIG.  33    is a block diagram of an example processor platform structured to execute the example computer readable instructions of  FIGS.  26  and/or  27    to implement the example system of  FIGS.  13 ,  14   , and/or  15 . 
         FIG.  34    is a block diagram of an example software distribution platform to distribute software (e.g., software corresponding to the example computer readable instructions of  FIGS.  16 ,  17 ,  18 ,  19 ,  20 ,  21 ,  22 ,  23 ,  24 ,  25 ,  26  and/or  27   ) to client devices such as consumers (e.g., for license, sale and/or use), retailers (e.g., for sale, re-sale, license, and/or sub-license), and/or original equipment manufacturers (OEMs) (e.g., for inclusion in products to be distributed to, for example, retailers and/or to direct buy customers). 
         FIG.  35    illustrates an overview of an edge cloud configuration for edge computing. 
         FIG.  36    illustrates operational layers among endpoints, an edge cloud, and cloud computing environments. 
         FIG.  37    illustrates an example approach for networking and services in an edge computing system. 
     
    
    
     The figures are not to scale. In general, the same reference numbers will be used throughout the drawing(s) and accompanying written description to refer to the same or like parts, elements, etc. Connection references (e.g., attached, coupled, connected, and joined) are to be construed broadly and may include intermediate members between a collection of elements and relative movement between elements unless otherwise indicated. As such, connection references do not necessarily infer that two elements are directly connected and in fixed relation to each other. 
     Unless specifically stated otherwise, descriptors such as “first,” “second,” “third,” etc. are used herein without imputing or otherwise indicating any meaning of priority, physical order, arrangement in a list, and/or ordering in any way, but are merely used as labels and/or arbitrary names to distinguish elements for ease of understanding the disclosed examples. In some examples, the descriptor “first” may be used to refer to an element in the detailed description, while the same element may be referred to in a claim with a different descriptor such as “second” or “third.” In such instances, it should be understood that such descriptors are used merely for identifying those elements distinctly that might, for example, otherwise share a same name. As used herein, “approximately” and “about” refer to dimensions that may not be exact due to manufacturing tolerances and/or other real world imperfections. As used herein “substantially real time” refers to occurrence in a near instantaneous manner recognizing there may be real world delays for computing time, transmission, etc. Thus, unless otherwise specified, “substantially real time” refers to real time+/−1 second. 
     DETAILED DESCRIPTION 
     Software Defined Silicon Architecture 
     Methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement and manage software defined silicon products, also referred to as silicon assets, are disclosed herein. Examples of silicon products include any type of semiconductor device, such as, computer processors, central processing unit(s) (CPUs), semiconductor chips, silicon hardware devices, etc., as well as circuit boards and/or systems employing such silicon products, etc. Software Defined Silicon (SDSi) as disclosed herein, which is also referred to as Software Defined Intelligent Silicon (SDISi), enables a hardware agnostic activation and entitlement management solution, which can realize additional market and monetization opportunities for silicon products. For example, silicon products can be released to the market with additional, dormant processing capacity and/or features (e.g., to support unexpected market shifts, future competitive pressures, etc.) SDSi provides a solution for customers to access those features and for the platform manufacturer to recover trapped revenue in shipped products post-sale. 
     As mentioned above, semiconductor device manufacturers currently ship semiconductor devices, such as microprocessors, with hardware and firmware features fixed, or locked, at the factory. For example, a semiconductor device manufacturer may implement a semiconductor device with one-time fuses that are activated, or blown, to disable some features at the factory, leaving those feature dormant and unusable in the shipped semiconductor device. Thus, even if additional, dormant hardware and/or firmware features are included in the shipped semiconductor devices, such dormant features are unable to be activated after the semiconductor devices leave the factory when such one-time fuse implementations are employed. To gain access to one or more of those dormant features, a customer would need to order, and a manufacturer would need to ship, new versions of the semiconductor devices that have the desired dormant feature(s) activated at the factory. To further complicate matters, the manufacturer may need to predefine and manage a numerous different stock keeping units (SKUs) to track the various combinations of different features that are able to be activated on its semiconductor devices, even if some of those combinations are never realized. 
     In contrast, SDSi provides a solution that enables activation, deactivation and management of silicon product features after the product has left the manufacturer&#39;s facility and control. Thus, for silicon product manufacturers, SDSi provides a monetization opportunity and an access to new routes to market. For example, SDSi enables manufacturers to capture additional revenue via one-time activation, on-demand activation and/or recurring subscription models that extend feature activation and entitlement management onto the customer premises, with the potential for income and profits beyond the initial product sale. Additionally or alternatively, SDSi enables manufacturers to take advantage of economies of scale by reducing the number of different silicon product versions that need to be manufactured. For example, through the use of SDSi, manufacturers can implement one version of a silicon product with a baseline set of features activated, and can then activate other dormant features as requested and purchased by customers for their particular applications. For customers, SDSi enables effective management of capital expenditures and operating expenditures through silicon-enabled intra-scalability and elasticity. For example, SDSi can streamline a customer&#39;s inventory by reducing the number of different silicon product versions that need to be stocked to support different applications. 
     SDSi systems, as disclosed herein, also enable efficient SKU management by providing the ability to activate SKUs permanently, semi-permanently and/or via capacity-on-demand, and to provide SKU assignments on a per-customer basis. SDSi systems, as disclosed herein, enable permanent or dynamic activation of dormant features (also referred to as “dark assets”) at a customer&#39;s premises without the need for a return merchandise authorization (RMA). In some examples, SDSI systems, as disclosed herein, also provide failure recovery solutions by activating dormant features to replace failed features on the silicon product. 
     These and other example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement and manage SDSi products are disclosed in greater detail below. 
     Software Defined Silicon Security 
     Methods, apparatus, systems, and articles of manufacture (e.g., physical storage media) for SDSi security are also disclosed herein. In some disclosed examples, SDSi solutions effectuate security features through at least one of peer-to-peer attestation or trusted execution environment (TEE) deployment. Maintaining trust within an SDSi solution is advantageous for silicon product manufacturers to ensure security of data (e.g., silicon product manufacturer owned cryptographical data). For example, the data can allow the unlocking or activation of SDSi features and security thereof is advantageous to protect revenue streams and prevent SDSi systems from being compromised by malicious actors. 
     In some disclosed examples, SDSi solutions effectuates system security by deploying a peer-to-peer attestation schema in a mesh network. For example, SDSi systems can communicate with each other to determine reputation information. In such disclosed examples, SDSi systems query other SDSIs systems for runtime measurements and identify compromised SDSi systems based on a comparison of the runtime measurements to known validated runtime measurements. In some such disclosed examples, SDSi systems identify an SDSi system to execute system functions, such as to facilitate entitlement/license processing and telemetry reporting, based on the reputation score. In some disclosed examples, SDSi systems improve system security by implementing re-certification processes to identify rogue and/or malicious SDSi systems. 
     In some disclosed examples, SDSi solutions effectuate system security by deploying TEEs within the SDSi systems or associated with the SDSi systems. For example, SDSi systems can explore an environment of a semiconductor device to determine security capabilities of the semiconductor device. In such disclosed examples, the security capabilities can include whether the semiconductor device supports deployment of one or more known TEEs, whether the semiconductor device has a capability to deploy a TEE component (e.g., trusted execution, trusted memory, trusted storage, etc.), etc. In some disclosed examples, SDSi systems deploy one of the known TEEs while, in some disclosed examples, SDSi systems compose a TEE based on one or more TEE components of which the semiconductor supports deployment thereof. In some disclosed examples, SDSi systems facilitate deployment of a TEE by translating an intent to deploy the TEE to one or more features of an associated semiconductor device. In such disclosed examples, SDSi systems translate the intent using one or more artificial intelligence (AI)/machine learning (ML) models. 
     These and other example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement and manage SDSi products are disclosed in greater detail below. 
     Device Enhancements 
     Device enhancements for software defined silicon implementations are also disclosed herein. As used herein, “the absolute time” refers to a particular clock and date reading (e.g., 11:11 PM EST, Jan. 1, 2020, etc.). As used herein, “the relative time” refers to an elapsed time between a fixed event (e.g., a time of manufacture of a device, etc.) and the current time. As used, herein a “time reference” refers to a singular absolute time reading and/or a singular relative time reading and may be used to generate a timestamp and/or an odometer reading. 
     As used herein, a “feature configuration” of a silicon product refers to the hardware, firmware, and/or physical features enabled on the silicon products. Feature configurations can, for example, include the number of cores of a processor that have been activated and/or the speed at which each core runs. As disclosed in further detail below, a license can be used to change the feature configuration of a silicon product. 
     As least some prior silicon products, such as central processing units (CPUs) and other semiconductor devices, are not able to provide/determine relative or absolute time references. For example, some existing CPUs lack internal clocks. Also, in at least some silicon products that include clocks, the clocks can be set and/or adjusted by a user of the machine, and, thusly, may not be reliable for determining absolute and/or relative time references. Further, some internal clocks (e.g., monotonic clocks, etc.) require power and, accordingly, cannot measure time if the silicon product and/or machine including the silicon product is powered off. Example SDSi systems disclosed herein utilize absolute and/or relative time references to enable or prohibit certain actions to ensure business and financial viability of feature activation decisions associated with the silicon product. In some examples, some silicon product features can be available only before or after a particular date and/or time from the time of manufacture of the processor. 
     Examples disclosed herein overcome the above-noted problems by adding one or more features to the silicon product, such that the feature has electrical properties that are time-dependent. In some examples disclosed herein, the electrical properties of the feature change in a known or predetermined manner as a function of time. In some examples disclosed herein, the electrical properties of the feature change when the silicon product is not powered on. In some examples disclosed herein, by determining the electrical properties of the feature at two separate points of time, the relative time between those points can be determined. In some examples disclosed herein, the electrical properties of the time-dependent features are measured at the time of manufacture and are stored with the date and time of manufacture. In such examples, the absolute time can be determined by adding the determined relative time between the current time and the time of manufacture to the date and time of manufacture. In some examples disclosed herein, the feature is implemented by a radioisotope. In some examples disclosed herein, the feature is implemented by a physical unclonable function (PUF) with time-varying electrical properties. As such, the examples disclosed herein provide a reliable and unfalsifiable measures of absolute and relative time references that do not require constant power to the silicon product and/or machine in which the silicon product is used. 
     Examples disclosed herein enable users, customers, and/or machine-manufacturers flexibility of changing the configuration of a processor after the silicon product has been manufactured. In some examples, the changing of the configuration of a silicon product can affect the operating conditions (e.g., thermal design power (TDP), etc.) of the silicon product, and, thusly, affect the lifespan and/or condition of the processor. As such, in some examples, changing the configuration of the silicon product can cause the silicon product to have a combination of features that damage the silicon product and/or reduce the lifespan of a silicon product to an unacceptable level. In some examples, the features activated in a given configuration can affect the operating conditions of a silicon product in an interdependent manner. For example, the number of active cores in a semiconductor device such as a CPU impacts the maximum frequency those cores can operate at, as well as the thermal design power of the semiconductor device. As such, to prevent unacceptable device degradation and damage, examples disclosed herein account for the effect of each feature on the operating conditions of the device. 
     Existing silicon products, such as CPUs, have limited internal storage. In existing techniques, the allowed configurations of a CPU are hard-coded into the CPU when the CPU is manufactured as multi-dimensional matrices. In some examples, given the number of features that may be enabled after manufacture, millions of potential combinations could be potentially enabled in a CPU. As such, in some examples, storing each allowable configuration in CPU memory can consume a significant amount of storage space of the CPU and severely limit the amount of memory available on the CPU for other functions and/or data. Adding additional memory to the CPU would increase manufacturing cost and/or the size of the CPU. 
     Examples disclosed herein overcome the above-noted problems by creating groups of features based on assigning weight(s) and/or value metric(s) to each feature in order to calculate a group score associated with a requested configuration. In such examples, the calculated group score can be compared to one or more thresholds to determine if the requested configuration allows for nominal operation of the silicon product. In some examples disclosed herein, the group score is compared to an enablement threshold. In such examples, if the group score does not satisfy the enablement threshold, the requested configuration results in operating conditions that may result in unacceptable degradation and/or damage to the silicon product and is, thusly, prohibited from being implemented. In some examples disclosed herein, the group score is compared to a warranty threshold. In such examples, if the group score does not satisfy the warranty threshold, the requested configuration results in operating conditions that void the warranty of the silicon product. In some examples disclosed herein, environmental factors (e.g., ambient temperature, available machine cooling, humidity, radiation, etc.) are incorporated into the determination of the group score. In some examples disclosed herein, multiple group scores are calculated for different unrelated feature-groups. In such examples, each group score is compared to different threshold values. In some examples disclosed herein, a machine learning model to refine and update the group score algorithm and/or weights for each feature using historic silicon product operating data. 
     Software Defined Silicon Architecture 
     Turning to the figures, a block diagram of an example system  100  to implement and manage SDSi products in accordance with teachings of this disclosure is illustrated in  FIG.  1   . The example SDSi system  100  of  FIG.  1    includes an example silicon product  105 , such as an example semiconductor device  105  or any other silicon asset  105 , that implement SDSi features as disclosed herein. Thus, the silicon product  105  of the illustrated example is referred to herein as an SDSi product  105 , such as an SDSi semiconductor device  105  or SDSi silicon asset  105 . The system  100  also includes an example manufacturer enterprise system  110  and an example customer enterprise system  115  to manage the SDSi product  105 . In the illustrated example of  FIG.  1   , at least some aspects of the manufacturer enterprise system  110  are implemented as cloud services in an example cloud platform  120 . 
     The example manufacturer enterprise system  110  can be implemented by any number(s) and/or type(s) of computing devices, servers, data centers, etc. In some examples, the manufacturer enterprise system  110  is implemented by a processor platform, such as the example processor platform  2800  of  FIG.  28   . Likewise, the example customer enterprise system  115  can be implemented by any number(s) and/or type(s) of computing devices, servers, data centers, etc. In some examples, the customer enterprise system  115  is implemented by a processor platform, such as the example processor platform  2900  of  FIG.  29   . The example cloud platform  120  can be implemented by any number(s) and/or type(s), such as Amazon Web Services (AWS®), Microsoft&#39;s Azure® Cloud, etc. In some examples, the cloud platform  120  is implemented by one or more edge clouds as described below in connection with  FIGS.  35 - 37   . Aspects of the manufacturer enterprise system  110 , the customer enterprise system  115  and the cloud platform  120  are described in further detail below. 
     In the illustrated example of  FIG.  1   , the SDSi product  105  is an SDSi semiconductor device  105  that includes example hardware circuitry  125  that is configurable under the disclosed SDSi framework to provide one or more features. For example, such features can include a configurable number of processor cores, a configurable clock rate from a set of possible clock rates, a configurable cache topology from a set of possible cache topologies, configurable coprocessors, configurable memory tiering, etc. As such, the hardware circuitry  125  can include one or more analog or digital circuit(s), logic circuits, programmable processor(s), programmable controller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), field programmable gate arrays (FPGAs), field programmable logic device(s) (FPLD(s)), etc., or any combination thereof. The SDSi semiconductor device  105  of  FIG.  1    also includes example firmware  130  and an example basic input/output system (BIOS)  135  to, among other things, provide access to the hardware circuitry  125 . In some examples, the firmware  130  and/or the BIOS  135  additionally or alternatively implement features that are configurable under the disclosed SDSi framework. The SDSi semiconductor device  105  of  FIG.  1    further includes an example SDSi asset agent  140  to configure (e.g., activate, deactivate, etc.) the SDSi features provided by the hardware circuitry  125  (and/or the firmware  130  and/or the BIOS  135 ), confirm such configuration and operation of the SDSi features, report telemetry data associated with operation of the SDSi semiconductor device  105 , etc. Aspects of the SDSi asset agent  140  are described in further detail below. 
     The system  100  allows a customer, such as an original equipment manufacturer (OEM) of computers, tablets, mobile phones, other electronic devices, etc., to purchase the SDSi semiconductor device  105  from a silicon manufacturer and later configure (e.g., activate, deactivate, etc.) one or more SDSi features of the SDSi semiconductor device  105  after it has left the silicon manufacturer&#39;s factory. In some examples, the system  100  allows the customer (OEM) to configure (e.g., activate, deactivate, etc.) the SDSi feature(s) of the SDSi semiconductor device  105  at the customer&#39;s facility (e.g., during manufacture of a product including the SDSi semiconductor device  105 ) or even downstream after customer&#39;s product containing the SDSi semiconductor device  105  has been purchased by a third party (e.g., a reseller, a consumer, etc.) 
     By way of example, consider an example implementation in which the semiconductor device  105  includes up to eight (8) processor cores. Previously, the number of cores activated on the semiconductor device  105  would be fixed, or locked, at the manufacturer&#39;s factory. Thus, if a customer wanted the semiconductor device  105  to have two (2) active cores, the customer would contract with the manufacturer to purchase the semiconductor device  105  with 2 active cores, and the manufacturer would ship the semiconductor device  105  with 2 cores activated, and identify the shipped device with a SKU indicating that 2 cores were active. However, the number of active cores (e.g., 2 in this example) could not be changed after the semiconductor device  105  left the manufacturer&#39;s factory. Thus, if the customer later determined that 4 (or 8) active cores were needed for its products, the customer would have to contract with the manufacturer to purchase new versions of the semiconductor device  105  with 4 (or 8) active cores, and the manufacturer would ship the new versions of the semiconductor device  105  with 4 (or 8) cores activated, and identify the shipped device with a different SKU indicating that 4 (or 8) cores were active. In such examples, the customer and/or the manufacturer may be left with excess inventory of the semiconductor device  105  with the 2-core configuration, which can incur economic losses, resource losses, etc. 
     In contrast, assume the number of processor cores activated on the semiconductor device  105  is an SDSi feature that can be configured in the example system  100  in accordance with teachings of this disclosure. In such an example, the customer could contract with the manufacturer to purchase the SDSi semiconductor device  105  with 2 active cores, and the manufacturer would ship the SDSi semiconductor device  105  with 2 cores activated, and identify the shipped device with a SKU indicating that 2 cores were active. After the device is shipped, if the customer determines that it would prefer that 4 cores were active, the customer management system  105  can contact the manufacturer enterprise system  110  via a cloud service implemented by the cloud platform  120  (represented by the line labeled  145  in  FIG.  1   ) to request activation of 2 additional cores. Assuming the request is valid, the manufacturer enterprise system  110  generates a license (also referred to as a license key) to activate the 2 additional cores, and sends the license to the customer management system  115  via the cloud service implemented by the cloud platform  120  (represented by the line labeled  145  in  FIG.  1   ) to confirm the grant of an entitlement to activate the 2 additional cores. The customer enterprise system  115  then sends the license (or license key) to the SDSi asset agent  140  of the SDSi semiconductor device  105  (via a network as represented by represented by the line labeled  155  in  FIG.  1   ) to cause activation of 2 additional cores provided by the hardware circuitry  125  of the SDSi semiconductor device  105 . In the illustrated example, the SDSi asset agent  140  reports a certificate back to the manufacturer enterprise system  110  (e.g., via an appropriate cloud service implemented by the cloud platform  120 , as represented by the line labeled  150  in  FIG.  1   ) to confirm activation of the 2 cores. In some examples, the SDSi asset agent  140  also reports the certificate back to the customer enterprise system  115  (e.g., via the network as represented by the line labeled  155  in  FIG.  1   ) to confirm activation of the 2 cores. In some examples, the SDSi asset agent  140  also reports telemetry data associated with operation of the SDSi semiconductor device  105  to the manufacturer enterprise system  110  (e.g., via the appropriate cloud service implemented by the cloud platform  120 , as represented by the line labeled  150  in  FIG.  1   ) and/or the customer enterprise system  115  (e.g., via the network as represented by the line labeled  155  in  FIG.  1   ). After successful activation is confirmed, the manufacturer then invoices the customer (e.g., via the manufacturer enterprise system  110  and the customer management system  115 ) for the newly activate features (e.g., 2 additional cores). In some examples, the manufacturer enterprise system  110  and/or the customer management system  115  determine a new SKU (e.g., a soft SKU) to identify the same SDSi semiconductor device  105  but with the new feature configuration (e.g., 4 cores instead of 2 cores). 
     If the customer later determines that it would prefer that 8 cores were active, the customer management system  115  can contact the manufacturer enterprise system  110  via the cloud service implemented by the cloud platform  120  (represented by the line labeled  145  in  FIG.  1   ) to request activation of the remaining 4 additional cores. Assuming the request is valid, the manufacturer enterprise system  110  generates another license (or license key) to activate the 4 additional cores, and sends the license to the customer management system  115  via the cloud service implemented by the cloud platform  120  (represented by the line labeled  145  in  FIG.  1   ) to confirm the grant of an entitlement to activate the 4 remaining cores. The customer enterprise system  115  then sends license (or license key) to the SDSi asset agent  140  of the SDSi semiconductor device  105  (e.g., via the network as represented by the line labeled  155  in  FIG.  1   ) to cause activation of the 4 remaining cores provided by the hardware circuitry  125  of the SDSi semiconductor device  105 . In the illustrated example, the SDSi asset agent  140  reports a certificate back to the manufacturer enterprise system  110  (e.g., via the appropriate cloud service implemented by the cloud platform  120 , as represented by the line labeled  150  in  FIG.  1   ) to confirm activation of the 4 remaining cores. In some examples, the SDSi asset agent  140  also reports the certificate back to the customer enterprise system  115  (e.g., via the network as represented by the line labeled  155  in  FIG.  1   ) to confirm activation of the 4 remaining cores. In some examples, the SDSi asset agent  140  reports telemetry data associated with operation of the SDSi semiconductor device  105  to the manufacturer enterprise system  110  (e.g., via the appropriate cloud service implemented by the cloud platform  120 , as represented by the line labeled  150  in  FIG.  1   ) and/or the customer enterprise system  115  (e.g., via the network as represented by the line labeled  155  in  FIG.  1   ). After successful activation is confirmed, the manufacturer then invoices the customer (e.g., via the manufacturer enterprise system  110  and the customer management system  115 ) for the newly activate features (e.g., the 4 additional cores). In some examples, the manufacturer enterprise system  110  and/or the customer management system  115  determine yet another new SKU (e.g., a soft SKU) to identify the same SDSi semiconductor device  105  but with the new feature configuration (e.g., 8 cores instead of 4 cores). 
     In the illustrated examples of  FIG.  1   , the communications between the manufacturer enterprise system  110  and the customer enterprise system  115 , between the manufacturer enterprise system  110  and the SDSi asset agent  140  of the SDSi semiconductor device  105 , and between the SDSi asset agent  140  of the SDSi semiconductor device  105  and the customer enterprise system  115  can be implemented by one or more networks. For example, such networks can include the Internet, one or more wireless (cellular, satellite, etc.) service provider networks, one or more wired (e.g., cable, digital subscriber line, optical fiber, etc.) networks, one or more communication links, busses, etc. 
     In some examples, the SDSi semiconductor device  105  is included in or otherwise implements an example edge node, edge server, etc., included in or otherwise implementing one or more edge clouds. In some examples, the SDSi semiconductor device  105  is included in or otherwise implements an appliance computing device. In some examples, the manufacturer enterprise system  110  is implemented by one or more edge node, edge server, etc., included in or otherwise implementing one or more edge clouds. In some examples, the manufacturer enterprise system  110  is implemented by one or more appliance computing devices. In some examples, the customer enterprise system  115  is implemented by one or more edge node, edge server, etc., included in or otherwise implementing one or more edge clouds. In some examples, the customer enterprise system  115  is implemented by one or more appliance computing devices. Examples of such edge nodes, edge servers, edge clouds and appliance computing devices are described in further detail below in connection with  FIGS.  35 - 37   . Furthermore, in some examples, such edge nodes, edge servers, edge clouds and appliance computing devices may themselves be implemented by SDSi semiconductor devices capable of being configured/managed in accordance with the teachings of this disclosure. 
     In some examples, the manufacturer enterprise system  110  communicates with multiple customer enterprise systems  115  and/or multiple SDSi semiconductor devices  105  via the cloud platform  120 . In some examples, the manufacturer enterprise system  110  communicates with multiple customer enterprise systems  115  and/or multiple SDSi semiconductor device(s)  105  via the cloud platform  120  through one or more edge servers/nodes. In either such example, the customer enterprise system(s)  115  and/or SDSi semiconductor device(s)  105  can themselves correspond to one or more edge nodes, edge servers, edge clouds and appliance computing devices, etc. 
     In some examples, the manufacturer enterprise system  110  may delegate SDSi license generation and management capabilities to one or more remote edge nodes, edge servers, edge clouds, appliance computing devices, etc., located within a customer&#39;s network domain. For example, such remote edge nodes, edge servers, edge clouds, appliance computing devices, etc., may be included in the customer enterprise system  115 . In some such examples, the manufacturer enterprise system  110  can delegate to such remote edge nodes, edge servers, edge clouds, appliance computing devices, etc., a full ability to perform SDSi license generation and management associated with the customer&#39;s SDSi semiconductor devices  105  provided the remote edge nodes, edge servers, edge clouds, appliance computing devices, etc., are able to communicate with manufacturer enterprise system  110 . However, in some examples, if communication with the manufacturer enterprise system  110  is disrupted, the remote edge nodes, edge servers, edge clouds, appliance computing devices may have just a limited ability to perform SDSi license generation and management associated with the customer&#39;s SDSi semiconductor devices  105 . For example, such limited ability may restrict the delegated SDSi license generation and management to supporting failure recovery associated with the SDSi semiconductor devices  105 . Such failure recovery may be limited to generating and providing licenses to configure SDSi features of a client&#39;s SDSi semiconductor device  105  to compensate for failure of one or more components of the SDSi semiconductor device  105  (e.g., to maintain a previously contracted quality of service). 
     A block diagram of an example system  200  that illustrates example implementations of the SDSi asset agent  140  of the SDSi silicon product  105 , the manufacturer enterprise system  110  and the customer enterprise system  115  included in the example system  100  of  FIG.  1    is illustrated in  FIG.  2   . The example SDSi asset agent  140  of  FIG.  2    includes an example agent interface  202 , example agent local services  204 , an example analytics engine  206 , example communication services  208 , an example agent command line interface (CLI)  210 , an example agent daemon  212 , an example license processor  214 , and an example agent library  218 . The example SDSi asset agent  140  of  FIG.  2    also includes example feature libraries  220 - 230  corresponding to respective example feature sets  232 - 242  implemented by the hardware circuitry  125 , firmware  130  and/or BIOS  135  of the SDSi semiconductor device  105 . The example manufacturer enterprise system  110  of  FIG.  2    includes an example product management service  252 , an example customer management service  254 , and an example SDSi feature management service  256 . The example manufacturer enterprise system  110  of  FIG.  2    also implements an example SDSi portal  262  and an example SDSi agent management interface  264  as cloud services in the cloud platform  120 . The example customer enterprise system  115  of  FIG.  2    includes an example SDSi client agent  272 , an example platform inventory management service  274 , an example accounts management service  276  and an example entitlement management service  278 . 
     In the illustrated example of  FIG.  2   , the agent interface  202  implements an interface to process messages sent between the SDSi asset agent  140  and the manufacturer enterprise system  110 , and between the SDSi asset agent  140  and the customer enterprise system  115 . The SDSi asset agent  140  of the illustrated example includes the agent local services  204  to implement any local services used to execute the SDSi asset agent  140  on the semiconductor device  105 . The SDSi asset agent  140  of the illustrated example includes the analytics engine  206  to generate telemetry data associated with operation of the semiconductor device  105 . Accordingly, the analytics engine  206  is an example of means for reporting telemetry data associated with operation of the semiconductor device  105 . The communication services  208  provided in the SDSi asset agent  140  of the illustrated example include a local communication service to enable the SDSi asset agent  140  to communicate locally with the other elements of the semiconductor device  105  and/or a product platform including the semiconductor device  105 . The communication services  208  also include a remote communication service to enable the SDSi asset agent  140  to communicate remotely with the SDSi agent management interface  264  of the manufacturer enterprise system  110  and the SDSi client agent  272  of the customer enterprise system  115 . The SDSi asset agent  140  of the illustrated example includes the agent CLI  210  to process commands entered locally to the semiconductor device  105  via a command line interface. The SDSi asset agent  140  of the illustrated example includes the license processor  214  to process license(s) received from the customer enterprise system  115  to configure (e.g., activate, deactivate, etc.) one or more SDSi features included in the feature sets  232 - 242  implemented by the hardware circuitry  125 , firmware  130  and/or BIOS  135  of the SDSi semiconductor device  105 . Accordingly, the license processor  214  is an example of means for activating or deactivating at least one feature of the semiconductor device  105  based on a license received via a network from a remote enterprise system. The SDSi asset agent  140  of the illustrated example includes the agent daemon  212  to securely execute the elements of the SDSi asset agent  140 . For example, the agent daemon  212  can execute one or more of the agent interface  202 , the agent local services  204 , the analytics engine  206 , the communication services  208 , the agent CLI  210  and/or the license processor  214  in a protected environment, such as a trusted execution environment (TEE), implemented by the semiconductor device  105 . The SDSi asset agent  140  of the illustrated example includes the agent library  218  to provide, among other things, hardware-agnostic application programming interfaces (APIs) to be used by the license processor  214  to invoke the respective, hardware-specific feature libraries  220 - 230  to configure (e.g., activate, deactivate, etc.), based on the received license data, one or more features in the corresponding example features sets  232 - 242  implemented by the hardware circuitry  125 , firmware  130  and/or BIOS  135  of the SDSi semiconductor device  105 . Accordingly, the hardware circuitry  125 , firmware  130  and/or BIOS  135  are examples of means for providing SDSi features in the SDSi semiconductor device  105 . In some examples, the agent library  218  and/or the hardware-specific feature libraries  220 - 230  also operate in a protected environment, such as a TEE, implemented by the semiconductor device  105 . Further details concerning the elements of the SDSi asset agent  140  of  FIG.  2    are described below. 
     In the illustrated example of  FIG.  2   , the manufacturer enterprise system  110  includes the example product management service  252  to manage the inventory, pricing, etc., of the products manufactured by the manufacturer of the SDSi semiconductor device  105 . The manufacturer enterprise system  110  of the illustrated example includes the customer management service  254  to manage customer accounts, billing, reconciliation, etc., for the manufacturer of the SDSi semiconductor device  105 . The manufacturer enterprise system  110  of the illustrated example includes the SDSi feature management service  256  to manage the configuration of SDSi feature(s) implemented by the silicon products manufactured by the manufacturer of the SDSi semiconductor device  105 . The manufacturer enterprise system  110  of the illustrated example implements the SDSi portal  262  to communicate (e.g., via a network) with the customer enterprise system  115 . The manufacturer enterprise system  110  of the illustrated example implements the SDSi agent management interface  264  to communicate (e.g., via a network) with the SDSi asset agent  140  of the SDSi semiconductor device  105 . Further details concerning the elements of the manufacturer enterprise system  110  of  FIG.  2    are described below. 
     In the illustrated example of  FIG.  2   , the customer enterprise system  115  includes the SDSi client agent  272  to communicate (e.g., via a network) with the manufacturer enterprise system  110  and the SDSi asset agent  140  of the SDSi semiconductor device  105 . The customer enterprise system  115  of the illustrated example includes the platform inventory management service  274  to manage the platforms offered by the customer (OEM), such as platforms that include the SDSi semiconductor device  105 . The customer enterprise system  115  of the illustrated example includes the accounts management service  276  to manage accounts, billings, reconciliations, etc., the customer has with manufacturers, downstream customers, etc., such as the manufacturer of the SDSi semiconductor device  105 . The customer enterprise system  115  of the illustrated example includes the entitlement management service  278  to manage licenses granted by manufacturers of SDSi products, such as the manufacturer of the SDSi semiconductor device  105 , to configure (e.g., activate, deactivate, etc.) SDSi features implemented by those products. Further details concerning the elements of the customer enterprise system  115  of  FIG.  2    are described below. 
     An example SDSi management lifecycle  300  capable of being implemented by the example systems  100  and/or  200  of  FIGS.  1 - 2    is illustrated in  FIG.  3   . The lifecycle  300  is described from the perspective of activating or deactivating an SDSI feature provided by the SDSi semiconductor device  105 , but also can be applied to any type of configuration change of an SDSI feature provided by the SDSi semiconductor device  105 . The lifecycle  300  begins at block  302  at which the SDSi client agent  272  of the customer enterprise system  115  sends a request to the SDSi portal  262  of the manufacturer enterprise system  110  to activate (or deactivate) an SDSI feature provided by the SDSi semiconductor device  105 . Accordingly, the SDSi portal  262  is an example of means for receiving a request to activate or deactivate a feature provided by the semiconductor device  105 . For example, the customer may access a customer management record for the SDSi semiconductor device  105  maintained by the platform inventory management service  274 , and modify the customer management record to invoke the SDSi client agent  272  to send the request. Accordingly, the SDSi client agent  272  is an example of means for sending a request to activate or deactivate an SDSi feature provided by the semiconductor device  105 . At block  304 , the SDSi portal  262  of the manufacturer enterprise system  110  receives the request sent by the SDSi client agent  272  of the customer enterprise system  115  to activate (or deactivate) the SDSI feature provided by the SDSi semiconductor device  105 . At block  306 , the SDSi agent management interface  264  sends a query to the SDSi asset agent  140  to confirm that the SDSi semiconductor device  105  supports the SDSi feature to be activated (or deactivated). For example, the SDSi feature management service  256  may process the customer request received via the SDSi portal  262  and invoke the SDSi agent management interface  264  to send the query. The agent interface  202  of the SDSi asset agent  140  receives the query and invokes the license processor  214  to generate a response. The license processor  214  analyzes the configuration of the hardware circuitry  125 , the firmware  130  and/or the BIOS  135  of the semiconductor device  105 , generates feature support verification information indicating whether the queried feature is supported by the semiconductor device  105 , and reports, via the agent interface  202 , a response including the feature support verification information to the SDSi agent management interface  264 . In some examples, rather than querying the SDSi asset agent  140  of the SDSi semiconductor device  105 , the SDSi agent management interface  264  accesses one or more databases and/or other data structures (e.g., based on device identifier and/or SKU information included in the feature request) that store specification/configuration data for the SDSi semiconductor device  105  to confirm whether the SDSi semiconductor device  105  supports the requested feature. 
     At block  308  of the lifecycle  300 , the SDSi agent management interface  264  receives the query response from the SDSi asset agent  140  (or from the queries database(s) and/or data structure(s)), which is processed by the SDSi feature management service  256 . If the response indicates the SDSi feature of interest is supported by the SDSi semiconductor device  105 , at block  310  the SDSi feature management service  256  generates a license to activate (or deactivate) the SDSi feature as requested. Accordingly, the SDSi feature management service  256  is an example of means for generating a license to be processed by the semiconductor device  105  to activate or deactivate an SDSi feature. Also, at block  312 , the SDSi feature management service  256  causes the license to be sent via the SDSi portal  262  to the SDSi client agent  272  of the customer enterprise system  115 . Accordingly, the SDSi client agent  272  is an example of means for receive a license from an enterprise management system to authorize activation or deactivation of an SDSi feature provided by the semiconductor device  105  In the illustrated example, the license generated at block  310  is associated with a license key and/or license data that specifies, for example, an identifier of the semiconductor device  105 , the SDSi feature to be activated (or deactivated), terms of the activation (or deactivation), such as whether this is a one-time feature activation (deactivation) or renewable activation subject to a subscription, a valid start window (e.g., X hours, where X is a numerical value, or some other duration) for invoking the license to activate (or deactivate) the SDSI feature, etc. At this point in the lifecycle  300 , the license generated at block  310  is treated as an unused license to activate (or deactivate) the SDSi feature, which is stored in a repository at the customer enterprise system  115  until the customer triggers use of the license to activate (or deactivate) the requested feature. For example, the SDSi feature management service  256  of the manufacturer enterprise system  110  can update a manufacturer management record maintained by the manufacturer for the semiconductor device  105  to include the license and/or license data generated at block  310 , Likewise, the entitlement management service  278  of the customer enterprise system  115  can update the customer management record maintained by the customer for the semiconductor device  105  to indicate receipt of the license along with the license details. Accordingly, the entitlement management service  278  is an example of means for updating a management record associated with the semiconductor device  105  based on a license. In some such examples, the entitlement management service  278  can be invoked by the customer to update the customer management record to trigger operation of the license to activate (or deactivate) the SDSi feature, which cause the SDSi client agent  272  of the customer enterprise system  115  to transmit (e.g., download) the license via the network  155  to the SDSi asset agent  140  of the semiconductor device  105 . 
     For example, upon receipt of a request at the SDSi client agent  272  to invoke the license, at block  314  the SDSi client agent  272  sends the license to the SDSi asset agent  140 . Accordingly, the SDSi client agent  272  is an example of means for sending a license to the semiconductor device  105 . The license is received by the agent interface  202 , which at block  316  invokes the license processor  214 . At block  316 , the license processor  214  processes the license data to identify the feature to be activated (or deactivated), and activates (or deactivates) the feature in accordance with the license data. For example, if the feature is a configurable number of processor cores, and the semiconductor device  105  was initialized to have a first number of the processor cores active (e.g., 2 of 8 cores are active) with remaining ones of the processor cores dormant (e.g., 6 of 8 cores are dormant), the license data may specify that a second number of dormant processor cores (e.g., 4 of the 6 dormant cores) are to be activated (e.g., in response to a request from the customer enterprise system  115  to activate the second number of dormant cores). The license data may also identify which of the dormant cores are to be activated. In such an example, the license processor  214  invokes the agent library  218  to activate the dormant cores specified in the license data. As another example, the SDSi asset agent  140  may later receive a second license from the SDSi client agent  272  of the customer enterprise system  115  that specifies a third number of the active processor cores (e.g., 2 of the 6 active cores) that are to be deactivated (e.g., with the second license being generated by the manufacturer enterprise system  110  in response to a request from the customer enterprise system  115  to deactivate the third number of active cores). The second license data may also identify which of the active cores are to be deactivated. In such an example, the license processor  214  invokes the agent library  218  to deactivate the active cores specified in the license data. In some examples, the license processor  214  may limit the number of cores able to be deactivated to not be greater the second number of dormant cores that were activated based on prior received license data. As yet another example, if the feature is a configurable clock rate, and the semiconductor device was initialized to activate a first clock rate from a set of possible clock rates, the license generated by the manufacturer enterprise system  110  and downloaded via the SDSi client agent  272  of the customer enterprise system  115  may identify a second clock rate different from the first clock rate that is to be activated (e.g., in response to a request from the customer enterprise system  115  to activate the second clock rate). In such an example, the license processor  214  invokes the agent library  218  to activate the second clock rate identified in the license data. 
     In some examples, a single license can configure multiple features across different feature categories. For example, a single license may include first license data to activate one or more additional cores, and second license to modify and/or otherwise adjust a clock rate of one or more cores. In such an example, the adjusted clock rate may be applied to one or more previously activated cores and/or one(s) of the one or more additional cores to be activated in response to the license processor  214  processing the license. Additionally or alternatively, in some examples, a single license can activate one or more features, and also deactivate one or more other features. 
     At block  318  of the lifecycle  300 , the analytics engine  206  of the SDSi asset agent  140  logs the SDSi feature activation (or deactivation) performed on the semiconductor device  105 . At block  320 , the analytics engine  206  captures an odometer reading representative of a present, local time maintained by the circuitry  125  (in combination with the firmware  135  and/or BIOS  140 ) of the semiconductor device  105 . For example, the circuitry  125  may utilize a counter, timer or other mechanism to implement an odometer to track the passage of time locally at the semiconductor device  105  (which is represented by the directed line  322  in  FIG.  3   .). At block  320 , the analytics engine  206  captures a value of the odometer to act as a timestamp of when the requested feature was activated (or deactivated). At block  324 , the analytics engine  206  generates a certificate to confirm the successful activation (or deactivation) of the requested SDSi feature. In the illustrated example, the certificate includes telemetry data associated with operation of the semiconductor device  105  and generated by the analytics engine  206  in response to activation (or deactivation) of the requested SDSi feature. In some examples, the telemetry data includes an indication of whether the feature activation (or deactivation) was a success, a status of the SDSi feature affected by the activation (or deactivation) (e.g., such as the presently configured number of cores that are active, the presently active clock rate, etc.), a first odometer reading (e.g., first timestamp) indicating when the feature activation (or deactivation) occurred, a second odometer reading (e.g., a second timestamp) indicating whether the certificate was generated, etc. 
     At block  326  of the lifecycle  300 , the analytics engine  206  reports, via the agent interface  202 , the certificate with the telemetry data in response to the activation (or deactivation) of the SDSi feature based on the received license data. In the illustrated example, the analytics engine  206  reports the certificate with the telemetry data to both the manufacturer enterprise system  110  and the customer enterprise system  115 . For example, at block  328 , the example SDSi agent management interface  264  of the manufacturer enterprise system  110  receives the certificate, and at block  330  provides it to the SDSi feature management service  256  of the manufacturer enterprise system  110 . Accordingly, the SDSi agent management interface  264  is an example of means for receiving a certificate from the semiconductor device  105  to confirm successful activation or deactivation of an SDSi feature. The SDSi feature management service  256  processes the certificate and included telemetry data to log the successful feature activation (or deactivation). Similarly, at block  332 , the SDSi client agent  272  of the customer enterprise system  115  receives the certificate and at block  334  provides it to the entitlement management service  278  of the customer enterprise system  115 . The entitlement management service  278  processes the certificate and included telemetry data to log the successful feature activation (or deactivation). In the illustrated example, at this point in the lifecycle  300 , the status of the feature activation (or deactivation) may be considered incomplete until verified by a subsequent certificate from the SDSi asset agent  140  (see blocks  336  and  338 ). 
     At block  340  of the lifecycle  300 , the SDSi agent management interface  264  of the manufacturer enterprise system  110  receives a subsequent certificate with updated telemetry data from the SDSi asset agent  140 . At block  342 , the subsequent certificate is provided to the SDSi feature management service  256  of the manufacturer enterprise system  110 . The SDSi feature management service  256  processes the certificate to obtain the updated telemetry data, and also obtains the prior telemetry data included in the previous certificate. At block  344 , the SDSi feature management service  256  accesses the odometer readings included in the telemetry data. At block  346 , the SDSi feature management service  256  compares the telemetry data and odometer reading to confirm the successful activation (or deactivation) (or, more generally, the successful configuration change) of the SDSi feature of interest. Accordingly, the SDSi feature management service  256  is an example of means for validating the successful activation or deactivation of an SDSi feature based on telemetry data. At block  348 , the customer management service  254  of the manufacturer enterprise system  110  generates an invoice for the successful activation (or deactivation) of the SDSi feature of interest, and sends it to the customer enterprise system  115  via the SDSi portal  262  for processing by the accounts management service  276 . In some examples, assuming the semiconductor device  105  is associated with a present SKU (e.g., a first SKU), after the requested SDSi feature is activated (or deactivated), the product management service  252  of the manufacturer enterprise system  110  generates a new SKU (e.g., a second SKU) and updates the manufacturer management record maintained for the semiconductor device  105  to associate the new SKU (second SKU) with the semiconductor device  105 . Accordingly, the product management service  252  is an example of means for updating a management record to associate a second SKU with the semiconductor device  105  after an SDSi feature is activated or deactivated. Additionally or alternatively, in some examples, assuming the semiconductor device  105  is associated with a present SKU (e.g., a first SKU), after the requested SDSi feature is activated (or deactivated), the platform inventory management service  274  of the customer enterprise system  115  generates a new SKU (e.g., a second SKU) and updates the customer management record maintained for the semiconductor device  105  to associate the new SKU (second SKU) with the semiconductor device  105 . Accordingly, the platform inventory management service  274  is an example of means for updating a management record to associate a second SKU with the semiconductor device  105  after an SDSi feature is activated or deactivated. 
     At block  350  of the lifecycle  300 , the entitlement management service  278  of the customer enterprise system  115  generates a request for status of the semiconductor device  105 , and sends the request via the SDSi client agent  272  to the SDSi asset agent  140 . Additionally or alternatively, the SDSi feature management service  256  of the manufacturer enterprise system  110  could generate the request for status of the semiconductor device  105 , and send the request via the SDSi agent management interface  264  to the SDSi asset agent  140 . In either case, at block  352 , the agent interface  202  receives the request and invokes the analytics engine  206  to generate a certificate in response to the request. In the illustrated example, the certificate includes updated telemetry data associated with operation of the semiconductor device  105  generated by the analytics engine  206  in response to the request. The updated telemetry data is timestamped with a local time corresponding to an odometer reading captured in response to the request. At blocks  354  and  356 , the SDSi agent management interface  264  receives the requested certificate with the updated telemetry data from the SDSi asset agent  140  and provides it to the SDSi feature management service  256  of the manufacturer enterprise system  110 . The SDSi feature management service  256  obtains the updated telemetry data, and also obtains the prior telemetry data for the semiconductor device  105 , and further accesses the odometer readings included in the telemetry data. At block  356 , the example SDSi feature management service  256  updates a history of the operational status of the semiconductor device  105  and uses the telemetry data to determine whether the semiconductor device  105  is operating properly. 
     Similarly, at block  360  of the lifecycle  300 , the SDSi client agent  272  receives the requested certificate with the updated telemetry data from the SDSi asset agent  140  and provides it to the entitlement management service  278  of the customer enterprise system  115 . The entitlement management service  278  obtains the updated telemetry data, and also obtains any prior telemetry data for the semiconductor device  105 , and further accesses the odometer readings included in the telemetry data. The entitlement management service  278  then updates a history of the operational status of the semiconductor device  105  and uses the telemetry data to determine whether the semiconductor device  105  is operating properly. In some examples, the accounts management service  276  of the customer enterprise system  115  updates, based on receipt of the certificate, the customer management record associated with the semiconductor device  105  to confirm establishment or conclusion of a payment obligation with the manufacturer of the semiconductor device  105 , such as the payment obligation associated with the invoice received from the manufacturer enterprise system  110  at block  348 . Accordingly, the accounts management service  276  is an example of means for updating a management record, based on a certificate, to confirm establishment or conclusion of a payment obligation with a manufacturer of the semiconductor device  105 . 
     As illustrated in the example lifecycle  300  of  FIG.  3   , the request to activate (or deactivate) the SDSI feature sent by the customer enterprise system  115  at block  302  and received by the manufacturer enterprise system  110  at block  304  can initiate a contract between the customer and the manufacturer. Later, the sending of the license to the customer enterprise system  115  at block  312  can be a trigger to start a payment obligation (see block  364 ). In some examples, the start of the payment obligation can be delayed until the feature is activated (or deactivated) in the semiconductor device  105  based on the license at block  316 . Later, the reporting at block  326  of the certificate in response to the activation (or deactivation) of the SDSi feature in the semiconductor device  105  can validate the payment obligation (see block  366 ). Later, the generation and receipt of the invoice at block  348  can trigger reconciliation of the payment obligation (see block  368 ). 
     The licenses generated by the manufacturer enterprise system  110  to activate (or deactivate) SDSi features in the semiconductor device  105  can support one-time activation, on-demand activation and/or recurring subscription models. For example, the license may include license data to instruct the license processor  214  of the SDSi asset agent  140  executing in the semiconductor device  105  to perform a one-time activation (or deactivation) of one or more features identified by the license data. In some examples, to support on-demand activation and/or recurring subscription models, the license generated by the manufacturer enterprise system  110  can include license data that instructs the license processor  214  to activate (or deactivate) the specified SDSi feature(s) in accordance with an express permit or an express deny control mechanism. For example, under an express permit control mechanism, the license processor  214  causes an SDSi feature that is activated based on the license to be deactivated upon expiration of a time period (e.g., tracked by a counter, clock, or other mechanism) unless an express permit control signal is received from the manufacturer enterprise system  110  (e.g., via the SDSi agent management interface  264 ) before the time period expires. Conversely, under an express deny control mechanism, the license processor  214  causes an SDSi feature that is activated based on the license to be remain active unless an express deny control signal is received from the manufacturer enterprise system  110  (e.g., via the SDSi agent management interface  264 ). In such an example, receipt of the express deny control signal causes the license processor  214  to deny access to the activated feature, such as, by deactivating the feature. 
     In some examples, the license processor  214  of the SDSi asset agent  140  executing in the semiconductor device  105  activates and deactivates SDSI features through the use of reprogrammable soft fuse(s), register(s), logic gate(s), etc. For example, such reprogrammable soft fuse(s), register(s), logic gate(s), etc., can be connected to control lines of the hardware blocks included in the hardware circuitry  125  of the semiconductor device  105  to implement the SDSi features, connected to control inputs read by the firmware  130  and/or BIOS  135  to enable/disable the SDSi features, etc. The license processor  214  can set and/or reset ones of the reprogrammable soft fuse(s), values of the register(s), input(s) of the logic gate(s), etc., to activate/deactivate different SDSi features of the semiconductor device  105 . 
     In some examples, the license processor  214  writes received license(s) and/or the license data included therein to a protected license memory region of the semiconductor device  105 . In some examples, the license data is encrypted and the license processor  214  decrypts the license data before writing it to the protected license memory region of the semiconductor device  105 . In some such examples, SDSi feature activation/deactivation responsive to a received license does not occur until the semiconductor device  105  reboots (e.g., via a soft reset, a hard reset, etc.) and the license data in the protected license memory region is read upon start-up. In some examples, the license processor  214  sets one or more particular locations of the protected license memory region to activate one or more SDSi features, and erases or overwrites the license data contained in those location(s) of the protected license memory region to deactivate those SDSi feature(s). For example, to deactivate a given SDSi feature, the license processor  214  may write random or otherwise garbage data to the location(s) associated with that feature in the protected license memory region, and rely on an error checking capability of the semiconductor device  105  that causes the given SDSi feature to remain disabled in response to such random or otherwise garbage data. 
     In some examples, the location(s) of the protected license memory region for deactivated SDSi feature(s) is(are) not erased or overwritten. Rather, in some such examples, to deactivate an SDSi feature, a deactivation license is appended to the list of licenses already stored in the protected license memory region for that SDSi feature. The newly received deactivation license in such an example overrides the actions of previously received licenses for that SDSi feature. In that way, the history of SDSi configuration operations (activations and deactivations) performed on the SDSi feature are stored by the semiconductor device  105  in the order the SDSi licenses were applied. In some examples, this information could be read by the customer. 
     Example certificates utilized in the example systems  100  and/or  200  of  FIGS.  1 - 2    to implement the example lifecycle  300  of  FIG.  3    are illustrated in  FIG.  4   . In the illustrated example of  FIG.  4   , the SDSi asset agent  140  associated with the semiconductor device  105  generates and reports a first example certificate  405  to the manufacturer enterprise system  110  and/or the customer enterprise system  115  in response to a first event (labeled “E 1 ” in  FIG.  4   ). The first event corresponds to activation of a first SDSi feature of the semiconductor device  105  (labeled “Feature  1 ” in  FIG.  4   ). In the illustrated example, the first certificate  405  identifies the first SDSi feature (“Feature  1 ”) and includes telemetry data. The telemetry data of the first certificate  405  indicates an activated status (labeled “A” in  FIG.  4   ) for the first SDSi feature (“Feature  1 ”), and includes a first timestamp (e.g., first odometer reading) having a value of “100,” which represents a local time at which the first SDSi feature (“Feature  1 ”) was activated in the semiconductor device  105 . The telemetry data of the first certificate  405  also indicates that another available SDSi feature (labeled “Feature  2 ” in  FIG.  4   ) is dormant. The telemetry data of the first certificate  405  further includes a second timestamp (e.g., second odometer reading) having a value of “110,” which represents a local time at which the first certificate  405  was generated. 
     In the illustrated example of  FIG.  4   , the SDSi asset agent  140  associated with the semiconductor device  105  generates and reports a second example certificate  410  to the manufacturer enterprise system  110  and/or the customer enterprise system  115  in response to a second event (labeled “E 2 ” in  FIG.  4   ). The second event corresponds to deactivation of the first SDSi feature of the semiconductor device  105  (labeled “Feature  1 ” in  FIG.  4   ). In the illustrated example, the second certificate  410  identifies the first SDSi feature (“Feature  1 ”) and includes updated telemetry data (in addition to the telemetry data included in the first certificate  405 ). The updated telemetry data of the second certificate  410  indicates a deactivated status (labeled “D” in  FIG.  4   ) for the first SDSi feature (“Feature  1 ”), and includes a third timestamp (e.g., third odometer reading) having a value of “192,” which represents a local time at which the first SDSi feature (“Feature  1 ”) was deactivated in the semiconductor device  105 . The updated telemetry data of the second certificate  410  also indicates that another available SDSi feature (labeled “Feature  2 ” in  FIG.  4   ) is still dormant. The updated telemetry data of the second certificate  410  further includes a fourth timestamp (e.g., fourth odometer reading) having a value of “213,” which represents a local time at which the second certificate  410  was generated. 
     In the illustrated example of  FIG.  4   , the SDSi asset agent  140  associated with the semiconductor device  105  generates and reports a third example certificate  415  to the manufacturer enterprise system  110  and/or the customer enterprise system  115  in response to a third event (labeled “E 3 ” in  FIG.  4   ). The third event corresponds to re-activation of the first SDSi feature of the semiconductor device  105  (labeled “Feature  1 ” in  FIG.  4   ). In the illustrated example, the third certificate  415  identifies the first SDSi feature (“Feature  1 ”) and includes updated telemetry data (in addition to the telemetry data included in the prior certificates  405  and  410 ). The updated telemetry data of the third certificate  415  indicates an activated status (labeled “A” in  FIG.  4   ) for the first SDSi feature (“Feature  1 ”), and includes a fifth timestamp (e.g., fifth odometer reading) having a value of “250,” which represents a local time at which the first SDSi feature (“Feature  1 ”) was re-activated in the semiconductor device  105 . The updated telemetry data of the third certificate  415  also indicates that another available SDSi feature (labeled “Feature  2 ” in  FIG.  4   ) is still dormant. The updated telemetry data of the third certificate  415  further includes a sixth timestamp (e.g., sixth odometer reading) having a value of “262,” which represents a local time at which the third certificate  415  was generated. 
     In the illustrated example of  FIG.  4   , the SDSi asset agent  140  associated with the semiconductor device  105  generates and reports a fourth example certificate  420  to the manufacturer enterprise system  110  and/or the customer enterprise system  115  in response to a fourth event (labeled “E 4 ” in  FIG.  4   ). The fourth event corresponds to activation of a second SDSi feature of the semiconductor device  105  (labeled “Feature  2 ” in  FIG.  4   ). In the illustrated example, the fourth certificate  420  identifies the second SDSi feature (“Feature  2 ”) and includes updated telemetry data (in addition to the telemetry data included in the prior certificates  405 - 415 ). The updated telemetry data of the fourth certificate  420  indicates an activated status (labeled “A” in  FIG.  4   ) for the second SDSi feature (“Feature  2 ”), and includes a seventh timestamp (e.g., seventh odometer reading) having a value of “390,” which represents a local time at which the second SDSi feature (“Feature  2 ”) was activated in the semiconductor device  105 . The updated telemetry data of the fourth certificate  420  further includes an eighth timestamp (e.g., eighth odometer reading) having a value of “405,” which represents a local time at which the fourth certificate  420  was generated. 
     In the illustrated example of  FIG.  4   , the SDSi asset agent  140  associated with the semiconductor device  105  generates and reports a fifth example certificate  425  to the manufacturer enterprise system  110  and/or the customer enterprise system  115  in response to a fifth event (labeled “E 5 ” in  FIG.  4   ). The fifth event corresponds to modification of the first SDSi feature (labeled “Feature  1 +” in  FIG.  4   ), and activation of a third SDSi feature of the semiconductor device  105  (labeled “Feature  3 ” in  FIG.  4   ). In the illustrated example, the fifth certificate  425  identifies the modified first SDSi feature (“Feature  1 +”) and the third SDSi feature (“Feature  3 ”), and includes updated telemetry data (in addition to the telemetry data included in the prior certificates  405 - 420 ). The updated telemetry data of the fifth certificate  425  indicates an activated status (labeled “A” in  FIG.  4   ) for the modified first SDSi feature (“Feature  1 +”), a de-activated status (labeled “D” in  FIG.  4   ) for the prior version of the first SDSi feature (“Feature  1 ”), and an activated status (labeled “A” in  FIG.  4   ) for the third SDSi feature (“Feature  3 ). The updated telemetry data includes a ninth timestamp (e.g., ninth odometer reading) having a value of “510,” which represents a local time at which the modified first SDSi feature (“Feature  1 +”) was activated, the prior version of the first SDSi feature (“Feature  1 ”) was deactivated, and the third SDSi feature (“Feature  3 ”) was activated in the semiconductor device  105 . The updated telemetry data of the fifth certificate  425  further includes a tenth timestamp (e.g., tenth odometer reading) having a value of “527,” which represents a local time at which the fifth certificate  425  was generated. 
     In the illustrated example of  FIG.  4   , the SDSi asset agent  140  associated with the semiconductor device  105  generates and reports a sixth example certificate  430  to the manufacturer enterprise system  110  and/or the customer enterprise system  115  in response to a first status request from the manufacturer enterprise system  110  and/or the customer enterprise system  115 . In the illustrated example, the sixth certificate  430  identifies the status history of the SDSi features provided by the semiconductor device  105 . For example, the sixth certificate  430  includes status and corresponding telemetry data to log the activation and de-activation events for the first SDSi feature (“Feature  1 ”) that occurred up to the time of the status request. The telemetry data of the sixth certificate  430  further includes a timestamp (e.g., odometer reading) having a value of “318,” which represents a local time at which the sixth certificate  430  was generated. 
     In the illustrated example of  FIG.  4   , the SDSi asset agent  140  associated with the semiconductor device  105  generates and reports a seventh example certificate  435  to the manufacturer enterprise system  110  and/or the customer enterprise system  115  in response to a second status request from the manufacturer enterprise system  110  and/or the customer enterprise system  115 . In the illustrated example, the seventh certificate  435  identifies the status history of the SDSi features provided by the semiconductor device  105 . For example, the seventh certificate  435  includes status and corresponding telemetry data to log the activation and de-activation events for the first SDSi feature (“Feature  1 ”), the modified first feature (Feature  1 +″), the second feature (Feature  2 ″) and the third feature (Feature  3 ″) that occurred up to the time of the status request. The telemetry data of the seventh certificate  435  further includes a timestamp (e.g., odometer reading) having a value of “604,” which represents a local time at which the seventh certificate  435  was generated. 
     An example process flow  500  performed by the example systems  100  and/or  200  of  FIGS.  1 - 2    to enable initial feature activation in the SDSi product  105  is illustrated in  FIG.  5   . The process flow  500  of the illustrated example begins with an example user  505  associated with a customer requesting registration of the customer for access to SDSi capabilities offered by a manufacturer of the SDSi product  105  (line  510 ). The manufacturer enterprise system  110  then engages with the customer enterprise system  115  to on-board the customer (lines  512 - 518 ). The manufacturer enterprise system  110  then receives a request to activate an SDSi feature of the SDSi product  105  and acknowledges the request (lines  520 - 526 ). As shown in the illustrated example, the feature activation request can be received from a source (e.g., computer device) separate from the customer enterprise system  115  or from the SDSi client agent  272  of the customer enterprise system  115 . In the illustrated example, the manufacturer enterprise system  110  further confirms with the customer enterprise system  115  that the SDSi feature activation request is valid (lines  528 - 530 ). 
     Assuming the SDSi feature activation request is valid, the manufacturer enterprise system  110  queries the SDSi product  105  to determine whether the requested SDSi feature to be activated is supported by the SDSi product  105  (lines  532 - 542 ). In the illustrated example, the manufacturer enterprise system  110  sends the query to the SDSi client agent  272  of the customer enterprise system  115 , which queries the SDSi product  105  for the status of the requested SDSi feature and returns a response to the manufacturer enterprise system  110 . However, in other examples, the manufacturer enterprise system  110  queries the SDSi product  105  without involving the SDSi client agent  272  or, more generally, the customer enterprise system  115 . For example, the SDSi agent management interface  264  of the manufacturer enterprise system  110  may send a query to the SDSi product  105  for the status of the requested SDSi feature and receive a response from the SDSi product  105 . 
     Assuming the requested SDSi feature is supported, the manufacturer enterprise system  110  generates a license to activate the requested SDSi feature in the SDSi product  105  (lines  544 - 546 ). The manufacturer enterprise system  110  also communicates with the customer enterprise system  115  to establish billing terms and other contractual obligations associated with activation of the requested SDSi feature (lines  548 - 554 ). The manufacturer enterprise system  110  then sends the generated license to the SDSi client agent  272  of the customer enterprise system  115  (line  556 ). Sometime later (e.g., when the customer is ready for the requested feature to be activated), the SDSi client agent  272  sends the license to the SDSi asset agent  140  of the SDSi product  105  (line  558 ). The SDSi asset agent  140  invokes the license processor  214  to process the received license to activate the requested SDSi feature (line  560 ). The license processor  214  invokes the analytics engine  206  to capture one or more odometer readings (lines  562 - 568 ), and then creates a confirmation certificate to confirm activation of the requested feature (lines  570 - 574 ). 
     The SDSi asset agent  140  of the SDSi product  105  then reports the confirmation certificate to the manufacturer enterprise system  110  and the customer enterprise system  115  (e.g., via the SDSi client agent  272  in the illustrated example) (lines  576 - 580 . The manufacturer enterprise system  110  and the customer enterprise system  115  process the received confirmation certificate, and the customer enterprise system  115  validates the start of a payment obligation responsive to activation of the requested SDSi feature (lines  582 - 588 ). 
     An example process flow  600  performed by the example systems  100  and/or  200  of  FIGS.  1 - 2    to enable additional feature activation in the SDSi product  105  is illustrated in  FIG.  6   . The process flow  600  of the illustrated example begins with the manufacturer enterprise system  110  receiving a request from the user  505  to activate an additional SDSi feature of the SDSi product  105  and acknowledging the request (lines  620 - 626 ). As shown in the illustrated example, the additional feature activation request can be received from a source (e.g., computer device) separate from the customer enterprise system  115  or from the SDSi client agent  272  of the customer enterprise system  115 . In the illustrated example, the manufacturer enterprise system  110  further confirms with the customer enterprise system  115  that the additional SDSi feature activation request is valid (lines  628 - 630 ). 
     Assuming the additional SDSi feature activation request is valid, the manufacturer enterprise system  110  queries the SDSi product  105  to determine whether the additional requested SDSi feature to be activated is supported by the SDSi product  105  (lines  632 - 642 ). In the illustrated example, the manufacturer enterprise system  110  sends the query to the SDSi client agent  272  of the customer enterprise system  115 , which queries the SDSi product  105  for the status of the requested SDSi feature and returns a response to the manufacturer enterprise system  110 . However, in other examples, the manufacturer enterprise system  110  queries the SDSi product  105  without involving the SDSi client agent  272  or, more generally, the customer enterprise system  115 . For example, the SDSi agent management interface  264  of the manufacturer enterprise system  110  may send a query to the SDSi product  105  for the status of the requested SDSi feature and receive a response from the SDSi product  105 . In the illustrated example, in addition to checking whether the requested feature is supported, the SDSi product  105  also validates the activation request against other policies (line  637 ). For example, such policies may confirm that the additional SDSi feature to be activated will not conflict with an earlier SDSi feature that was activated. 
     Assuming the requested additional SDSi feature is supported, the manufacturer enterprise system  110  generates a license to activate the additional SDSi feature in the SDSi product  105  (lines  644 - 646 ). The manufacturer enterprise system  110  also communicates with the customer enterprise system  115  to establish billing terms and other contractual obligations associated with activation of the additional SDSi feature (lines  648 - 654 ). The manufacturer enterprise system  110  then sends the generated license to the SDSi client agent  272  of the customer enterprise system  115  (line  656 ). Sometime later (e.g., when the customer is ready for the additional feature to be activated), the SDSi client agent  272  sends the license to the SDSi asset agent  140  of the SDSi product  105  (line  658 ). The SDSi asset agent  140  invokes the license processor  214  to process the received license to activate the requested SDSi feature (line  660 ). The license processor  214  also invokes the analytics engine  206  to collect telemetry data for the SDSi features of the SDSi product  105  (line  661 ) and capture one or more odometer readings (lines  662 - 672 ), and then creates a confirmation certificate to confirm activation of the requested feature (lines  673 - 675 ). 
     The SDSi asset agent  140  of the SDSi product  105  then reports the confirmation certificate to the manufacturer enterprise system  110  and the customer enterprise system  115  (e.g., via the SDSi client agent  272  in the illustrated example) (lines  676 - 680 . The manufacturer enterprise system  110  and the customer enterprise system  115  process the received confirmation certificate, and the customer enterprise system  115  validates the start of a payment obligation responsive to activation of the requested SDSi feature (lines  682 - 688 ). In the illustrated example, the SDSi asset agent  140  of the SDSi product  105  also reports SDSi feature status telemetry (e.g., included in subsequent certificates) to the manufacturer enterprise system  110  (e.g., via the SDSi client agent  272  in the illustrated example) (lines  690 - 694 ). 
     An example process flow  700  performed by the example systems  100  and/or  200  of  FIGS.  1 - 2    to enable feature deactivation in the SDSi product  105  is illustrated in  FIG.  7   . The process flow  700  of the illustrated example begins with the manufacturer enterprise system  110  receiving a request from the user  505  to deactivate an SDSi feature of the SDSi product  105  and acknowledging the request (lines  720 - 726 ). As shown in the illustrated example, the feature deactivation request can be received from a source (e.g., computer device) separate from the customer enterprise system  115  or from the SDSi client agent  272  of the customer enterprise system  115 . In the illustrated example, the manufacturer enterprise system  110  further confirms with the customer enterprise system  115  that the SDSi feature deactivation request is valid (lines  728 - 730 ). 
     Assuming the SDSi feature deactivation request is valid, the manufacturer enterprise system  110  queries the SDSi product  105  to determine whether the requested SDSi feature to be deactivated is supported by the SDSi product  105  (lines  732 - 742 ). In the illustrated example, the manufacturer enterprise system  110  sends the query to the SDSi client agent  272  of the customer enterprise system  115 , which queries the SDSi product  105  for the status of the requested SDSi feature and returns a response to the manufacturer enterprise system  110 . However, in other examples, the manufacturer enterprise system  110  queries the SDSi product  105  without involving the SDSi client agent  272  or, more generally, the customer enterprise system  115 . For example, the SDSi agent management interface  264  of the manufacturer enterprise system  110  may send a query to the SDSi product  105  for the status of the requested SDSi feature and receive a response from the SDSi product  105 . In the illustrated example, in addition to checking whether the requested feature is supported, the SDSi product  105  also validates the deactivation request against other policies (line  737 ). For example, such policies may confirm that the SDSi feature to be deactivated will not cause a conflict with remaining active SDSi features, will not violate a specified base SDSi feature state for SDSi product  105 , etc. 
     Assuming the SDSi feature to be deactivated is supported, the manufacturer enterprise system  110  generates a license to deactivate the SDSi feature in the SDSi product  105  (lines  744 - 746 ). The manufacturer enterprise system  110  also communicates with the customer enterprise system  115  to establish billing terms and other contractual obligations associated with deactivation of the SDSi feature (lines  748 - 754 ). The manufacturer enterprise system  110  then sends the generated license to the SDSi client agent  272  of the customer enterprise system  115  (line  756 ). Sometime later (e.g., when the customer is ready for the feature to be deactivated), the SDSi client agent  272  sends the license to the SDSi asset agent  140  of the SDSi product  105  (line  758 ). The SDSi asset agent  140  invokes the analytics engine  206  to collect telemetry data for the SDSi product  105  before feature deactivation (line  759 ), and then invokes the license processor  214  to process the received license to deactivate the requested SDSi feature (line  760 ). The analytics engine  206  also captures one or more odometer readings and collects telemetry data for the remaining active SDSi features of the SDSi product  105  (lines  762 - 774 ), which are used by the license processor  214  to create a confirmation certificate to confirm deactivation of the requested feature (lines  775 ). 
     The SDSi asset agent  140  of the SDSi product  105  then reports the confirmation certificate to the manufacturer enterprise system  110  and the customer enterprise system  115  (e.g., via the SDSi client agent  272  in the illustrated example) (lines  776 - 780 . The manufacturer enterprise system  110  and the customer enterprise system  115  process the received confirmation certificate, and the customer enterprise system  115  validates the start of a payment obligation responsive to deactivation of the requested SDSi feature (lines  782 - 788 ). In the illustrated example, the SDSi asset agent  140  of the SDSi product  105  also reports SDSi feature status telemetry (e.g., included in subsequent certificates) to the manufacturer enterprise system  110  (e.g., via the SDSi client agent  272  in the illustrated example) (lines  790 - 794 ). 
     An example process flow  800  performed by the example systems  100  and/or  200  of  FIGS.  1 - 2    to provide customer-initiated feature usage status and billing reconciliation is illustrated in  FIG.  8   . The process flow  800  of the illustrated example begins with the manufacturer enterprise system  110  receiving a request from the user  505  for SDSi feature status of the SDSi product  105  (lines  802 - 804 ). As shown in the illustrated example, the additional feature activation request can be received from a source (e.g., computer device) separate from the customer enterprise system  115 , or can be received at the SDSi client agent  272  thereby bypassing the manufacturer enterprise system  110 . In the illustrated example, the feature status request is received by the SDSi client agent  272 , which sends the request to the SDSi asset agent  140  of the SDSi product  105  (line  806 ). In response to the feature status request, the SDSi asset agent  140  invokes the analytics engine  206  to collect telemetry data, current and past feature status, and odometer readings (lines  808 - 816 ). The SDSi asset agent  140  the reports the SDSi feature status of the SDSi product  105  to the customer enterprise system  115  (line  818 ), and more detailed telemetry status to the manufacturer enterprise system  110  (lines  820 - 822 ). In the illustrated example, the customer enterprise system  115  collects the SDSi feature status data from the SDSi product  105  and retains the status to perform feature usage status and billing reconciliation (lines  824 - 826 ). 
     An example process flow  900  performed by the example systems  100  and/or  200  of  FIGS.  1 - 2    to provide manufacturer-initiated feature usage status and billing reconciliation is illustrated in  FIG.  9   . The process flow  900  of the illustrated example begins with the manufacturer enterprise system  110  beginning an invoicing process based on SDSi feature usage associated with the SDSi product  105  (line  902 ). The manufacturer enterprise system  110  then send a request for SDSi feature status of the SDSi product  105  (line  904 ). In the illustrated example, the feature status request is received by the SDSi client agent  272 , which sends the request to the SDSi asset agent  140  of the SDSi product  105  (line  906 ). In response to the feature status request, the SDSi asset agent  140  invokes the analytics engine  206  to collect telemetry data, current and past feature status, and odometer readings (lines  908 - 916 ). The SDSi asset agent  140  the reports the SDSi feature status and telemetry to the manufacturer enterprise system  110  (lines  920 - 922 ). In the illustrated example, the manufacturer enterprise system  110  uses the SDSi feature status data and telemetry from the SDSi product  105  to perform feature usage status and billing, and sends an invoice to the customer enterprise system  115  (lines  928 - 932 ). The customer enterprise system  115  then reconciles the invoice against stored SDSI feature status and telemetry for the SDSi product  105 , approves payment, and sends payment (or a payment completion record) to the manufacturer enterprise system  110  (lines  934 - 940 ). 
     Although the examples of  FIGS.  1 - 9    are illustrated as including a single SDSi silicon product  105  (SDSi semiconductor device  105 ), a single manufacturer enterprise system  110  in associated with a single cloud platform  120 , and a single customer enterprise system  115 , SDSi frameworks and architectures as disclosed herein are not limited thereto. For example, any number(s) and/or type(s) of SDSi silicon products  105  (SDSi semiconductor devices  105 ) can be configured and managed in the example systems  100  and  200  described above. Additionally or alternatively, any number of manufacturer enterprise systems  110  and/or cloud platforms  120  can be included in the systems  100  and  200  described above to manage respective SDSi silicon products  105  (SDSi semiconductor devices  105 ) manufactured by different silicon manufacturers. Additionally or alternatively, any number of client enterprise systems  115  can be included in the systems  100  and  200  described above to manage SDSi silicon products  105  (SDSi semiconductor devices  105 ) purchased by different customers (e.g., OEMs). Also, in some examples, the client enterprise systems  115  can include multiple SDSi client agents  272 . For example, the client enterprise systems  115  can configure different SDSi client agents  272  to manage different groups of one or more SDSi products  105 . 
     Software Defined Silicon Security 
     A block diagram of an example system  1000  to implement and manage SDSi products in accordance with teachings of this disclosure is illustrated in  FIG.  10   . The example SDSi system  1000  of  FIG.  10    includes a plurality of the example silicon products  105  of  FIG.  1    (e.g., a plurality of the SDSi semiconductor devices  105  of  FIG.  1   , a plurality of the semiconductor assets  105  of  FIG.  1   , etc.), such as a first example silicon product  105 A (referred to herein as the “first semiconductor device  105 A”), a second example silicon product  105 B (referred to herein as the “second semiconductor device  105 B”), and a third example silicon product  105 C (referred to herein as the “third semiconductor device  105 A”). The silicon products  105 A-C include respective instantiations of the SDSi asset agent  140  of  FIG.  1   , such as a first example SDSi asset agent  140 A, a second example SDSi asset agent  140 B, and a third example SDSi asset agent  140 C. The SDSi asset agents  140 A-C of the example of  FIG.  10   , and/or, more generally, the semiconductor products  105 A-C of  FIG.  10   , implement SDSi features as disclosed herein. The system  1000  also includes the example manufacturer enterprise system  110  of  FIG.  1    and the example customer enterprise system  115  of  FIG.  1 W  to manage the SDSi asset agents  140 A-C of  FIG.  10   , and/or, more generally, the semiconductor products  105 A-C of  FIG.  10   . In the illustrated example of  FIG.  10   , at least some aspects of the manufacturer enterprise system  110  and/or the customer enterprise system  115  are implemented as cloud services in the example cloud platform  120  of  FIG.  1   . 
     In the illustrated example of  FIG.  10   , the manufacturer enterprise system  110  is in communication with the customer enterprise system  115  via a cloud service implemented by the cloud platform  120  (represented by the line labeled  145  of  FIG.  1   ). In the illustrated example of  FIG.  10   , the SDSi semiconductor devices  105 A-C are in communication with the manufacturer enterprise system  110  via a cloud service implemented by the cloud platform  120  (represented by the line labeled  150  of  FIG.  1   ). In the illustrated example of  FIG.  10   , the SDSi semiconductor devices  105 A-C are in communication with the customer enterprise system  115  via a cloud service implemented by the cloud platform  120  (represented by the line labeled  155  of  FIG.  1   ). 
     In the illustrated example of  FIG.  10   , the SDSi semiconductor devices  105 A-C are in communication with each other via an example mesh network (e.g., a meshnet)  1002 . For example, one or more of the SDSi semiconductor devices  105 A-C are in communication with one or more of the SDSi semiconductor devices  105 A-C using a mesh networking topology (e.g., a Hybrid Wireless Mesh Protocol (HWMP), Dynamic Source Routing, Associativity-Based Routing, Zone Routing Protocol, etc.) to deliver data in a wireless network (e.g., a wireless local area network (WLAN)). In such examples, one or more of the SDSi semiconductor devices  105 A-C are in communication with one(s) of the SDSi semiconductor devices  105 A-C in a direct, dynamic, and/or non-hierarchically architecture to route data between some or all of the semiconductor devices  105 A-C. 
     In the illustrated example of  FIG.  10   , the mesh network  1002  may implement one or more peer-to-peer (P2P) security protocols where either side (e.g., an initiating one of the semiconductor devices  105 A-C, a receiving one of the semiconductor devices  105 A-C, etc.) can initiate authentication to the other side, or both sides can initiate authentication simultaneously. In some examples, when peers (e.g., peer SDSi asset agents  140 A-C, peer semiconductor devices  105 A-C, etc.) discover each other, they take part in an authentication process, such as a secure password-based authentication and/or key establishment protocol. In some examples, the authentication process may be implemented by a Simultaneous Authentication of Equals (SAE). In such examples, if SAE completes successfully, each peer knows the other peer possesses the authentication (e.g., the mesh password) and, as a by-product of the SAE exchange, the two peers establish a cryptographically strong key. In such examples, the cryptographic key is used to establish a secure peering session and derive a session key to protect mesh traffic, including routing traffic, during the secure peering session. 
     In some examples, peers can authenticate each other and/or otherwise be authenticated based on asymmetric encryption algorithms and/or symmetric encryption algorithms. For example, the SDSi asset agents  140 A-C may decrypt/encrypt data in a data packet using the Advanced Encryption Standard (AES) that includes using a block cipher (e.g., the AES-128 block cipher, the AES-192 block cipher, the AES-256 block cipher, etc.) to decrypt/encrypt the data included in the data packet. In some examples, the SDSi asset agents  140 A-C can decrypt/encrypt data using an AES cipher block chaining (AES-CBC) algorithm, a ciphertext feedback (AES-CFB) algorithm, an AES output feedback (AES-OFB) algorithm, a 2-byte CBC message authentication code (CBC-MAC) algorithm, a Galois MAC (GMAC) algorithm, or a keyed-Hashing MAC (HMAC) algorithm. Additionally or alternatively, the SDSi asset agents  140 A-C may decrypt/encrypt data using any other symmetric algorithm. In some examples, the SDSi asset agents  140 A-C can decrypt/encrypt data using an asymmetric encryption technique by using two independent keys, a first key to encrypt the data packet and a second key to decrypt the data packet. For example, the SDSi asset agents  140 A-C may decrypt/encrypt a data packet of interest using a Diffie-Hellman key exchange, or a Rivest, Shamir and Adleman (RSA) algorithm. Additionally or alternatively, the SDSi asset agents  140 A-C may decrypt/encrypt the data packet of interest using any other asymmetric encryption technique. 
     In some examples, the SDSi asset agents  140 A-C authenticate and/or otherwise validate connections between the SDSi asset agents  140 A-C. For example, the first SDSi asset agent  140 A determines whether the second SDSi asset agent  140 B has authorization to exchange data with the first SDSi asset agent  140 A. In such examples, the first SDSi asset agent  140 A can receive a first data packet from the second SDSi asset agent  140 B corresponding to a request by the second semiconductor device  105 B to communicate with the first semiconductor device  105 A. In response to receiving the request, the first SDSi asset agent  140 A can send a second data packet including a signature request to the second SDSi asset agent  140 B. For example, the signature request corresponds to a signature associated with an Elliptic Curve Digital Signature Algorithm (ECDSA). In response to receiving the signature request, the second SDSi asset agent  140 B can generate and transmit a third data packet including the signature to the first SDSi asset agent  140 A via the mesh network  1002 . The first SDSi asset agent  140 A can authenticate the signature provided by the second SDSi asset agent  140 B and validate subsequent data packet transfers between the first SDSi asset agent  140 A and the second SDSi asset agent  140 B. In some examples, the first SDSi asset agent  140 A may authenticate the second SDSi asset agent  140 B through any other Digital Signature Algorithm (DSA). 
     In some examples, the SDSi asset agents  140 A-C communicate with each other via the mesh network  1002  to determine and/or otherwise obtain reputation information or score(s) (e.g., agent reputation score(s)). In some examples, a reputation score is indicative or representative of a level of trustworthiness associated with an agent (e.g., the SDSi asset agents  140 A-C) of a semiconductor device (e.g., the semiconductor devices  105 A-C). In some examples, an agent with the highest reputation score is chosen to facilitate a system function, such as issuing a license or reporting telemetry data (e.g., reporting telemetry data to the manufacturer enterprise system  110  and/or the customer enterprise system  115 ). Advantageously, to avoid locking into one of the SDSi asset agents  140 A-C, which at some point may become compromised and turn rogue or otherwise malicious, the SDSi asset agents  140 A-C may determine that a subsequent license be issued by a different one of the SDSi asset agents  140 A-C. 
     In some examples, one or more of the activated features of one(s) of the semiconductor devices  105 A-C may deactivate (e.g., periodically deactivate, asynchronously deactivate, etc.) to invoke a re-certification process of the deactivated one(s) of the semiconductor devices  105 A-C. Advantageously, one(s) of the SDSi asset agents  140 A-C can be located both in an intranet and on the Internet to effectuate high availability and performance maintaining a relatively high level of security. 
     In some examples, the SDSi asset agents  140 A-C improve security of the system  1000  by deploying a TEE in which to execute secure application code and/or protect data of interest (e.g., silicon product manufacturer owned cryptographical data). For example, a TEE is a compute or computing execution context wherein resources, such as process data, memory, storage, input(s)/output(s) (I/O(s)), etc., are isolated and protected from untrusted and/or unauthorized access. In such examples, the TEE takes the form of an entire operating system or portion(s) thereof, such as application code running or executing in an isolated environment, such as that provided by Intel® Software Guard Extensions (SGX). 
     In some examples, the SDSi asset agents  140 A-C deploy a TEE based on known TEEs deployable by one(s) of the semiconductor devices  105 A-C. For example, the first SDSi asset agent  140 A invokes the first semiconductor device  105 A to deploy a first TEE included in the first semiconductor device  105 A in response to determining that the first TEE is supported and/or otherwise deployable by the first semiconductor device  105 A. In other examples, the first SDSi asset agent  140 A invokes the second semiconductor device  105 B to deploy the first TEE included in the second semiconductor device  105 B in response to determining that the first TEE is not supported and/or otherwise deployable by the first semiconductor device  105 A but is supported and/or otherwise deployable by the second semiconductor device  105 B. In some such examples, the first TEE is a hardware or hardware-based TEE, a software or software-based TEE, or a combination thereof. 
     In some examples, the SDSi asset agents  140 A-C assemble, compose, and/or otherwise generate a TEE based on one or more TEE components. For example, the TEE components correspond to compute capabilities that exist within the compute environment that represent a part, portion, or component of a TEE, such as trusted execution, trusted memory, trusted storage, etc. In such examples, the TEE components are either located on a platform local to an agent making a TEE deployment request (e.g., the first semiconductor device  105 A when the first SDSi asset agent  140 A makes the TEE deployment request) or available through a network connection (e.g., the mesh network  1002 , the cloud platform  120 , etc.). In some examples, the SDSi asset agents  140 A-C generate a TEE based on one or more identified TEE components in response to determining that a known TEE or a previously generated or deployed TEE is not identified. 
     In some examples, the SDSi asset agents  140 A-C deploy a TEE in response to translating an intent or intended outcome expected by a request. For example, the request is a change in a configuration, a requirement, etc., associated with availability (e.g., redundancy, a number failures-to-tolerate (FTT), etc.), machine learning, performance, reliability, security, etc., parameters of the system  1000 . In such examples, a user (e.g., a customer, a computing device associated with the customer, a server, etc.) generates a request to adjust a configuration of the system  1000  based on one or more requirements. In some such examples, the SDSi asset agents  140 A-C execute one or more artificial intelligence (AI)/machine learning (ML) models to translate and/or otherwise convert an intent or intended outcome from the one or more requirements of the request into one or more features of one(s) of the semiconductor devices  105 A-C. For example, the SDSi asset agents  140 A-C execute the one or more AI/ML models to translate a request to an intent to improve security of the system  1000 , map the intent to one or more features (e.g., configurable features, security features, etc.) of one(s) of the semiconductor devices  105 A-C, and deploy the one or more features based on the mapping. In such examples, the SDSi asset agents  140 A-C maps the intent to one or more security features, such as a TEE supported by the first semiconductor device  105 A, and invokes the first semiconductor device  105 A to deploy the first TEE. Advantageously, the user may generate the request to adjust operation of the system  1000  without requiring the user to have in-depth knowledge of hardware and/or software configurations of the system  1000 . 
     As used herein, availability refers to the level of redundancy required to provide continuous operation expected for workload(s) (e.g., computing workload(s), computational task(s), etc.) executed by the system  1000 . As used herein, performance refers to the computer processing unit (CPU) operating speeds (e.g., CPU gigahertz (GHz)), memory (e.g., gigabytes (GB) of random access memory (RAM)), mass storage (e.g., GB hard drive disk (HDD), GB solid state drive (SSD)), and/or power capabilities to execute the workload(s). As used herein, security refers to hardware (e.g., a processor executing security decryption, encryption, monitoring services, etc., a firewall device, a hardware-based TEE, etc.), software (e.g., a software-based TEE, a virtual sandbox, etc.) and/or firmware (e.g., a firmware-based TEE) that can be deployed to protect the system  1000  or portion(s) thereof. 
       FIG.  11    depicts a block diagram of an example system  1100  that illustrates example implementations of the SDSi asset agent  140  and/or one(s) of the SDSi asset agents  140 A-C, the manufacturer enterprise system  110 , and the customer enterprise system  115  included in the example system  100  of  FIG.  1    and/or the example system  1000  of  FIG.  10   . In the illustrated example of  FIG.  11   , the manufacturer enterprise system  110  includes the example SDSi feature management service  256  of  FIG.  2    and an example manufacturer trusted agent determiner  1102 . In the illustrated example of  FIG.  11   , the manufacturer enterprise system  110  includes the SDSi feature management service  256  to determine whether to renew license(s) associated with one(s) of the SDSi asset agents  140 A-C to improve and/or otherwise effectuate security of the system  1000  of the example of  FIG.  10   . 
     In the illustrated example of  FIG.  11   , the manufacturer enterprise system  110  includes the manufacturer trusted agent determiner  1102  to determine a reputation score (e.g., an agent reputation score) for one(s) of the SDSi asset agents  140 A-C of  FIG.  11   . For example, the SDSi feature management service  256  determines whether to renew license(s) associated with one(s) of the SDSi asset agents  140 A-C. In such examples, the manufacturer trusted agent determiner  1102  determines a reputation score for respective one(s) of the SDSi asset agents  140 A-C based on whether the license(s) are renewed. In some such examples, the manufacturer trusted agent determiner  1102  determines a first reputation score for the first SDSi asset agent  140 A based on the determination to renew the license(s) associated with the first SDSi asset agent  140 A. In some such examples, the manufacturer trusted agent determiner  1102  determines a second reputation score for the first SDSi asset agent  140 A based on the determination not to renew the license(s) associated with the first SDSi asset agent  140 A. In some examples, the second reputation score is less than the first reputation score because a determination not to renew the license(s) is indicative of the first SDSi asset agent  140 A being compromised and/or otherwise acting not in accordance with accepted or typical behavior of an SDSi agent. Additionally or alternatively, the manufacturer enterprise system  110  of the example of  FIG.  11    may include the example product management service  252  and/or the example customer management service  254  of the example of  FIG.  2   . 
     In the illustrated example of  FIG.  11   , the system  1100  includes the example SDSi portal  262  and the example SDSi agent management interface  264  of  FIG.  2   . In the example of  FIG.  11   , the example SDSi portal  262  and the example SDSi agent management interface  264  are implemented as cloud services in the cloud platform  120 . In the illustrated example of  FIG.  11   , the example customer enterprise system  115  of  FIG.  2    and/or  FIG.  10    includes the example SDSi client agent  272 , the example platform inventory management service  274 , and the example entitlement management service  278  of  FIG.  2   . Additionally or alternatively, the example customer enterprise system  115  of  FIG.  11    may include the example accounts management service  276  of the example of  FIG.  2   . 
     In the illustrated example of  FIG.  11   , the system  1100  includes an example implementation of the SDSi asset agent  140  of  FIG.  1   , and/or more generally, the semiconductor device  105  of  FIG.  1   , and/or an example implementation of the SDSi asset agents  140 A-C of  FIG.  10   , and/or, more generally, the semiconductor devices  105 A-C of  FIG.  10   . In the illustrated example of  FIG.  11   , the SDSi asset agents  140 A-C include the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication service(s)  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218 , and the example feature libraries  220 - 230  of  FIG.  2    corresponding to the respective example feature sets  232 - 242  of  FIG.  2    implemented by the example hardware circuitry  125 , the example firmware  130 , and/or the example BIOS  135  of  FIG.  2   . 
     In the illustrated example of  FIG.  11   , the analytics engine  206  includes an example trusted agent determiner  1104 , an example certificate validator  1106 , an example anomaly detector  1108 , and example anomaly detection machine learning (ML) model(s)  1110 . 
     In the illustrated example of  FIG.  11   , the analytics engine  206  includes the trusted agent determiner  1104  to determine and/or obtain reputation information (e.g., agent reputation information) associated with one(s) of the SDSi asset agents  140 A-C. In some examples, the trusted agent determiner  1104  attests and/or otherwise executes attestation process(es) to determine a level of trustworthiness of the one(s) of the SDSi asset agents  140 A-C. In such examples, the trusted agent determiner  1104  identifies one(s) of the SDSi asset agents  140 A-C as trusted agent(s) (e.g., trusted SDSi agent(s)) based on the attestation. For example, in response to the first SDSi asset agent  140 A being identified as a trusted agent, the first SDSi asset agent  140 A can be an issuer (e.g., a trusted issuer) and/or a sender (e.g., a trusted sender) or transmitter (e.g., trusted transmitter). For example, an issuer is an agent that can obtain a license from the manufacturer enterprise system  110 . In such examples, the issuer issues the license to a requesting one of the SDSi asset agents  140 A-C. In some such examples, the issuer generates a certificate to confirm the successful activation or deactivation of the requested feature. In some examples, a sender is an agent that obtains a request to activate or deactivate a feature from one of the SDSi asset agents  140 A-C and transmits the request to the manufacturer enterprise system  110 . 
     In some examples, the trusted agent determiner  1104  determines whether a request has been received to activate and/or deactivate feature(s) of one(s) of the semiconductor devices  105 A-C. In such examples, the trusted agent determiner  1104  identifies one(s) of the SDSi asset agents  140 A-C as trusted agent(s). In some such examples, the trusted agent determiner  1104  selects a trusted agent from the identified trusted agent(s) to be a sender and/or an issuer to facilitate an issuance of corresponding license(s) based on the request. 
     In some examples, in response to a receipt of the request, the trusted agent determiner  1104  determines agent reputation score(s) of one(s) of the SDSi asset agents  140 A-C to identify trusted agent(s). In some examples, the trusted agent determiner  1104  identifies one(s) of the SDSi asset agents  140 A-C as trusted agent(s) based on the agent reputation scores. In some examples, the trusted agent determiner  1104  identifies the first SDSi asset agent  140 A as a first trusted agent based on a first agent reputation score of the first SDSi asset agent  140 A satisfying a threshold (e.g., a reputation score threshold, an agent reputation score threshold, a trusted reputation score threshold, a trusted agent reputation score threshold, etc.). For example, the trusted agent determiner  1104  determines that the first SDSi asset agent  140 A has a first agent reputation score of 95 (e.g., a 95 out of a possible maximum agent reputation score of 100) and identifies the first SDSi asset agent  140 A as the first trusted agent based on the first agent reputation score of 95 being greater than the threshold of 80 and, thus, satisfying the threshold. However, the first agent reputation score may be any other number, the first agent reputation score may be with respect to any other possible maximum agent reputation score, and/or the threshold may be any other number or representation of a threshold. 
     In some examples, the trusted agent determiner  1104  selects the first SDSi asset agent  140 A as a trusted sender and/or a trusted issuer to execute the request for license(s) based on the first SDSi asset agent  140 A having the highest agent reputation score of the SDSi asset agents  140 A-C. In some examples, the trusted agent determiner  1104  selects the first SDSi asset agent  140  as a trusted sender and/or a trusted issuer based on a list of previously used trusted agents to ensure requests are distributed more evenly across all available and/or otherwise identified trusted agents. For example, the trusted agent determiner  1104  maintains a list (e.g., a trusted agent list) of N previously used trusted agents. In such examples, the trusted agent list includes the SDSi asset agents  140 A-C. In some such examples, the trusted agent determiner  1104  queries and/or otherwise searches the list and determines based on the query that the first SDSi asset agent  140 A has not been previously used to transmit an entitlement request. As used herein, entitlement requests refer to requests to activate, deactivate, etc., SDSi features of an asset, such as the semiconductor device  105 . In some examples, an entitlement refers to an authorization to activate, deactivate, etc., one or more SDSi features of an asset, and a license refers to data and/or other things that cause activation, deactivation, etc., on the asset of the one or more SDSi features for which an entitlement has been granted. In some examples, the trusted agent determiner  1104  identifies the first SDSi asset agent  140 A as the trusted agent in response to identifying the first SDSi asset agent  140  as not previously transmitting an entitlement request or, in some examples, in response to identifying the first SDSi asset agent  140 A as not being used more recently than the second SDSi asset agent  140 B and the third SDSi asset agent  140 C. 
     In some examples, the trusted agent determiner  1104  determines agent reputation score(s) based on agent reputation score data. In such examples, the trusted agent determiner  1104  selects one of the SDSi asset agents  140 A-C of interest to process. For example, the trusted agent determiner  1104  selects the first SDSi asset agent  140 A to process. In such examples, the trusted agent determiner  1104  obtains certificate(s), renewed certificate(s), and/or agent information from the first SDSi asset agent  140 A. In some such examples, the agent information includes an identifier of the first semiconductor device  105 A, telemetry data reported by the first SDSi asset agent  140 A, etc. In some such examples, the telemetry data includes an indication of whether a feature activation (or deactivation) was a success, a status of the SDSi feature affected by the activation (or deactivation) (e.g., such as the presently configured number of cores that are active, the presently active clock rate, etc.), a first odometer reading (e.g., first timestamp) indicating when the feature activation (or deactivation) occurred, a second odometer reading (e.g., a second timestamp) indicating whether the certificate was generated, etc. 
     In some examples, an initial agent reputation score is assigned by the manufacturer trusted agent determiner  1102  of the manufacturer enterprise system  110  to one(s) of the SDSi asset agents  140 A-C based on an attestation protocol and/or an agent registration process. For example, the manufacturer trusted agent determiner  1102  issues an initial agent reputation score of 100 to the SDSi asset agents  140 A-C because the level of trustworthiness is at a maximum after being commissioned from the silicon manufacturer. In some examples, the manufacturer trusted agent determiner  1102  invokes the SDSi agent management interface  264  to distribute the initial agent reputation scores to the SDSi asset agents  140 A-C so that respective ones of the SDSi asset agents  140 A-C maintain their own lists of agent reputation scores. In some examples, the trusted agent determiner  1104  of the SDSi asset agents  140 A-C monitor and/or otherwise observe different ones of the SDSi asset agents  140 A-C to identify anomalies, outliers, etc., associated with a number of licenses issued, a frequency of status broadcasts by the SDSi asset agents  140 A-C, substantial changes in value(s) of feature(s) in license(s) issued by the SDSi asset agents  140 A-C, etc. For example, the trusted agent determiner  1104  of the second SDSi asset agent  140 B and the third SDSi asset agent  140 C of the mesh network  1002  of  FIG.  10    detect that the first SDSi asset agent  140 A of the mesh network  1002  is abnormally behaving based on at least the criteria described above. In such examples, the trusted agent determiner  1102  of the second SDSi asset agent  140 B lowers an agent reputation score of the first SDSi asset agent  140 A by a first amount in a first list maintained by the second SDSi asset agent  140 B and the trusted agent determiner  1104  of the third SDSi asset agent  140 C lowers the agent reputation score of the first SDSi asset agent  140 A by a second amount in a second list maintained by the third SDSi asset agent  140 C. In some examples, the first amount is the same as the second amount while in other examples the first amount is different from the second amount. 
     In some examples, the trusted agent determiner  1104  of the second SDSi asset agent  140 B and the third SDSi asset agent  140 C of the mesh network  1002  of  FIG.  10    detect that the first SDSi asset agent  140 A of the mesh network  1002  is behaving as expected based on at least the criteria described above. For example, in response to the first SDSi asset agent  140 A issuing a license to the first semiconductor device  105 A that is consistent with typical behavior of an SDSi agent and/or is consistent with typical behavior of the first SDSi asset agent  140 A, the trusted agent determiner  1104  of the second SDSi asset agent  140 B and the third SDSi asset agent  140 C determine an agent reputation score of the first SDSi asset agent  140 A, an adjustment to the agent reputation score, etc., and/or a combination thereof. In some examples, the trusted agent determiner  1104  of the second SDSi asset agent  140 B increases an agent reputation score of the first SDSi asset agent  140 A by a first amount in a first list maintained by the second SDSi asset agent  140 B and the trusted agent determiner  1104  of the third SDSi asset agent  140 C increases the agent reputation score of the first SDSi asset agent  140 A by a second amount in a second list maintained by the third SDSi asset agent  140 C. For example, the trusted agent determiner of the second SDSi asset agent  140 B and the third SDSi asset agent  140 C determine the first amount and the second amount based on an issued certificate broadcasted from the first SDSi asset agent  140 A to the mesh network  1002 . In some examples, the first amount is the same as the second amount while in other examples the first amount is different from the second amount. 
     In some examples, the trusted agent determiner  1104  determine an agent reputation score based on whether one of the SDSi asset agents  140 A-C reported a certificate to the manufacturer enterprise system  110 . For example, the first SDSi asset agent  140 A broadcasts a certificate to the mesh network  1002 . In such examples, the second SDSi asset agent  140 B and/or the third SDSi asset agent  140 C report the broadcasted certificate to the manufacturer enterprise system  110 . In some such examples, the manufacturer trusted agent  1102  invokes the SDSi agent management interface  264  to generate an alert, inform, notify, etc., the second SDSi asset agent  140 B and/or the third SDSi asset agent  140 C that the first SDSi asset agent  140 A reported the certificate to the manufacturer enterprise system  110 . In some such examples, the trusted agent determiner  1104  of the second SDSi asset agent  140 B and/or the third SDSi asset agent  140 C increase an agent reputation score of the first SDSi asset agent  140 A in the list(s) of the second SDSi asset agent  140 B and/or the third SDSi asset agent  140 C. In other examples, the manufacturer trusted agent  1102  invokes the SDSi agent management interface  264  to generate an alert, inform, notify, etc., the second SDSi asset agent  140 B and/or the third SDSi asset agent  140 C that the first SDSi asset agent  140 A did not report the certificate to the manufacturer enterprise system  110 . In some such examples, the trusted agent determiner  1104  of the second SDSi asset agent  140 B and/or the third SDSi asset agent  140 C decrease an agent reputation score of the first SDSi asset agent  140 A in the list(s) of the second SDSi asset agent  140 B and/or the third SDSi asset agent  140 C because the first SDSi asset agent  140 A is abnormally behaving and/or otherwise not operating in a trustworthy manner. 
     In some examples, the trusted agent determiner  1104  blocks one(s) of the SDSi asset agents  140 A-C based on an agent reputation score (e.g., by adding them to a blocked list). For example, the first SDSi asset agent  140 A and the second SDSi asset agent  140 B blocks the third SDSi asset agent  140 C in response to an agent reputation score of the third SDSi asset agent  140 C satisfying a blocked threshold. In such examples, the trusted agent determiner  1104  of the first SDSi asset agent  140 A determines that the agent reputation score satisfies the blocked threshold based on the agent reputation score being lower than the blocked threshold. In some such examples, in response to the blocked threshold being satisfied, the first SDSi asset agent  140 A adds the third SDSi asset agent  140 C to a blocked list maintained by the first SDSi asset agent  140 A. In some such examples, the trusted agent determiner  1104  of the first SDSi asset agent  140 A determines that the agent reputation score of 60 satisfies the blocked threshold of 80 based on the agent reputation score of 60 being lower than the blocked threshold of 80. 
     In some examples, the trusted agent determiner  1104  allows access to one(s) of the SDSi asset agents  140 A-C based on an agent reputation score (e.g., by adding them to an allowed list). For example, the first SDSi asset agent  140 A and the second SDSi asset agent  140 B allow access to the third SDSi asset agent  140 C in response to an agent reputation score of the third SDSi asset agent  140 C satisfying an allowed threshold. In such examples, the trusted agent determiner  1104  of the first SDSi asset agent  140 A determines that the agent reputation score of the third SDSi asset agent  140 C satisfies the allowed threshold based on the agent reputation score being higher than the whitest threshold (or a blocked threshold). In some such examples, in response to the allowed threshold being satisfied, the first SDSi asset agent  140 A adds the third SDSi asset agent  140 C to an allowed list maintained by the first SDSi asset agent  140 A. In some such examples, the trusted agent determiner  1104  of the first SDSi asset agent  140 A determines that the agent reputation score of 90 satisfies the allowed threshold of 80 based on the agent reputation score of 90 being higher than the allowed threshold of 80. 
     In some examples, the trusted agent determiner  1104  evaluates whether an agent reputation score associated with an SDSi asset agent satisfies the allowed threshold in response to obtaining a license from the SDSi asset agent. For example, the first SDSi asset agent  140 A may receive a license from the second SDSi asset agent  140 B. In such examples, the first SDSi asset agent  140 A may compare the agent reputation score of the second SDSi asset agent  140 B to the allowed threshold prior to invoking the license to ensure that the license has not been compromised by a malicious actor. In some such examples, the first SDSi asset agent  140 A invokes the license in response to determining that the agent reputation score satisfies the allowed threshold. In some such examples, the first SDSi asset agent  140 A discards the license in response to determining that the agent reputation score does not satisfy the allowed threshold. 
     Advantageously, in some examples, the trusted agent determiner  1104  communicates with different one(s) of the SDSi asset agents  140 A-C for improved security. For example, to prevent the first semiconductor device  105 A from communicating with only the second SDSi agent  140 B, which may be compromised and/or otherwise controlled by a malicious actor or attacker, the first SDSi agent  140 A may cycle through one(s) of the SDSi agents  140 A-C. For example, the first SDSi agent  140 A may cycle through one(s) of the SDSi agents  140 A-C in a random pattern, a round robin pattern, etc., or any other type of pattern. In some examples, the first SDSi agent  140 A may communicate with the second SDSi agent  140 B during a first interaction, the third SDSi agent  140 C during a second interaction, etc. In some such examples, after communicating with the second SDSi agent  140 B during the first interaction, the first SDSi agent  140 A may initiate a counter, a timer, etc., to determine a time period during which the second SDSi agent  140 B is not to be communicated with. In response to the counter, the timer, etc., triggering the end of the time period, the first SDSi agent  140 A may communicate again with the second SDSi agent  140 B. 
     In some examples, the trusted agent determiner  1104  determines an agent reputation score based on a fingerprint (e.g., an agent fingerprint) determined by a runtime measurement. For example, the SDSi asset agents  140 A-C request (e.g., periodically request, asynchronously request, etc.) runtime measurements from different ones of the SDSi asset agents  140 A-C. Example runtime measurements include a hashed and/or otherwise cryptographically generated measurement of an application (e.g., application code, machine readable instructions, etc.) in memory, a counter value (e.g., a hardware counter value, a CPU counter value, etc.), etc. In some examples, the runtime measurements are indicative of, representative of, and/or otherwise correspond to a fingerprint of an agent, and/or, more generally, a semiconductor device. 
     In some examples, the trusted agent determiner  1104  determines an agent reputation score based on a comparison of a fingerprint obtained from a first agent to a stored fingerprint in a second agent. For example, the trusted agent determiner  1104  of the first SDSi asset agent  140 A queries the second SDSi asset agent  140 B for a runtime measurement. In such examples, the trusted agent determiner  1104  of the second SDSi asset agent  140 B obtains a runtime measurement associated with hardware of the second semiconductor device  105 B, such as a CPU counter value, and cryptographically signs the runtime measurement to generate a digital signature (e.g., an electronic signature). For example, the trusted agent determiner  1104  of the second SDSi asset agent  140 B generates the digital signature by executing an algorithm (e.g., a cryptographic algorithm, an encryption algorithm, etc.) to transform first data (e.g., a runtime measurement) into second data (e.g., cryptographical data, cipher text, unreadable cipher text, etc.) that is unreadable to an unauthorized device or user. In some examples, the second SDSi asset agent  140 B transmits the digital signature, the signed runtime measurement (e.g., the cryptographically signed runtime measurement), etc., to the first SDSi asset agent  140 A. In such examples, the trusted agent determiner  1104  of the first SDSi asset agent  140 A decrypts the digital signature by executing an algorithm (e.g., a cryptographic algorithm, a decryption algorithm, etc.) to make the underlying runtime measurement readable to the trusted agent determiner  1104 . In some such examples, the trusted agent determiner  1104  uses a key (e.g., an asymmetric key, a symmetric key, etc.) to make the cryptographically protected runtime measurement readable. 
     In some examples, the trusted agent determiner  1104  of the first SDSi asset agent  140 A compares the decrypted runtime measurement to a stored runtime measurement that is associated with the second SDSi asset agent  140 B. For example, the stored runtime measurement is included in and/or otherwise stored in a file (e.g., a signed known good measurement file) that is stored in the mesh of the SDSi asset agents  140 A-C, and/or, more generally, the mesh network  1002 . In such examples, the file is returned to the one of the SDSi asset agents  140 A-C that is acting and/or otherwise operating as a certification or validation authority on call of the one of the SDSi asset agents  140 A-C being attested. In some such examples, different versions of the file are stored in different ones of the SDSi asset agents  140 A-C due to untimely synchronization. The respective files have a security version number (SVN). In some examples, the file having the highest SVN, which is indicative of the most recent or most up-to-date file, is returned to the certification authority for the purposes of runtime measurement attestation. 
     In some examples, in response to determining that the measurements match based on the comparison, the trusted agent determiner  1104  of the first SDSi asset agent  140 B increases an agent reputation score of the second SDSi asset agent  140 B. In other examples, in response to determining that the measurements do not match based on the comparison, the trusted agent determiner  1104  of the first SDSi asset agent  140 A decreases the agent reputation score of the second SDSi asset agent  140 B. In some such examples, the mismatch of the runtime measurements is indicative that the second SDSi asset agent  140 B has been compromised, manipulated (e.g., maliciously manipulated), etc., and/or is otherwise a rogue or malicious agent or actor. 
     In some examples, the trusted agent determiner  1104  implements means for determining a trusted agent. For example, the means for determining determines respective reputation scores associated with a plurality of agents in a mesh network, the plurality of agents associated with a plurality of semiconductor devices, respective ones of the semiconductor devices including circuitry configurable to provide one or more features. In such examples, the means for determining selects, based on the respective reputation scores, a first agent from the plurality of the agents to transmit a request to activate or deactivate at least one of the one or more features. In some examples, the request is a first request transmitted at a first time, and the means for determining determines whether the first agent transmitted a second request at a second time prior to the first time, and in response to determining that the first agent did not transmit the second request, selects the first agent to transmit the first request. In some examples, the means for determining queries the first agent for a cryptographically signed runtime measurement associated with a resource of the semiconductor device, compares the cryptographically signed runtime measurement to a validated cryptographically signed runtime measurement, broadcasts a validation result based on the comparison to the mesh network to cause the plurality of the agents to store the validation result, and in response to the validation result indicating a match, adds the first agent to an allowed list. In some examples, the means for determining blocks the first agent in response to the validation result not indicating a match, and drops future broadcasts from the first agent. In this example, the means for determining is implemented by any processor structured to perform the corresponding operation by executing software or firmware, or hardware circuit (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, a PLD, a FPLD, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate. 
     In some examples, the agent interface  202  implements means for interfacing with an agent. For example, the means for interfacing broadcasts an activation or deactivation of one or more features to a mesh network to cause the means for determining to update the reputation score of the first agent in response to the request. In this example, the means for interfacing is implemented by any processor structured to perform the corresponding operation by executing software or firmware, or hardware circuit (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, a PLD, a FPLD, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate. 
     In the illustrated example of  FIG.  11   , the analytics engine  206  includes the certificate validator  1106  to renew certificate(s) associated with trusted agent(s) of the mesh network  1002 . In some examples, to effectuate security of the system  1100  of  FIG.  11   , trusted agent(s) are requested (e.g., periodically requested, asynchronously requested, etc.) to renew certificates by invalidating feature activation. For example, the certificate validator  1106  of the first SDSi asset agent  140 A identifies the second semiconductor device  105 B to validate by renewing certificate(s) associated with the second semiconductor device  105 B. In such examples, the certificate validator  1106  determines certification information, such as a current asset status, activated feature(s), and/or license issuer(s) associated with certificate(s) of the second semiconductor device  105 B. 
     In some examples, the certificate validator  1106  implements means for validating a certificate. For example, the means for determining determines a first reputation score of a first agent based on a renewal of a license issued to a first semiconductor device of a plurality of semiconductor devices associated with the first agent, and the means for validating determines an asset status of the first semiconductor device, determines a feature activated on the first semiconductor device, determine an issuer of the license that invoked an activation of the feature, cryptographically signs renew request data including data associated with at least one of the asset status, the feature, or the issuer, and transmits the cryptographically signed renew request data to cause a server to determine whether to facilitate the renewal of the license. In some examples, the means for validating, in response to obtaining a renewed license from the server, provisions the renewed license to the first semiconductor device and generates a renewal certificate. In such examples, the means for interfacing broadcasts the renewal certificate to the mesh network, and the means for determining updates the reputation score of the first agent based on the renewal certificate. In this example, the means for validating is implemented by any processor structured to perform the corresponding operation by executing software or firmware, or hardware circuit (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, a PLD, a FPLD, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate. 
     In some examples, the certificate validator  1106  de-activates the activated feature(s) based on the certificate information. In such examples, the certificate validator  1106  of the second SDSi asset agent  140 B transmits a renew request (e.g., a renew certificate request) to the manufacturer enterprise system  110 . In some such examples, the certificate validator  1106  of the second SDSi asset agent  140 B cryptographically signs the renew request which, in some examples, includes the certificate information or portion(s) thereof. In response to obtaining the renew request, the SDSi feature management service  256  determines whether to renew the certificate(s) previously issued to the second semiconductor device  105 B. For example, the SDSi feature management service  256  determines to renew the request based on an agent reputation score of the second SDSi asset agent  140 B satisfying a threshold, determining that the certificate(s) were previously reported to the mesh network  1002  and/or the manufacturer enterprise system  110 , etc., and/or a combination thereof. In some examples, the SDSi feature management service  256  determines not to renew the request based on the agent reputation score of the second SDSi asset agent  140 B not satisfying the threshold, determining that the certificate(s) were not previously reported to the mesh network  1002  and/or the manufacturer enterprise system  110 , etc., and/or a combination thereof, which is/are indicative of the second SDSi asset agent  140 B being a rogue and/or otherwise compromised agent. 
     In some examples, in response to the SDSi feature management service  256  determining not to renew the request, the SDSi management service  256  invokes the SDSi agent management interface  264  to generate an alert, inform, notify, etc., one(s) of the SDSi asset agents  140 A-C that the second SDSi asset agent  140 B is not having the certificate(s) renewed. In such examples, the first SDSi asset agent  140 A, the third SDSi agent  140 C, etc., decrease an agent reputation score of the second SDSi asset agent  140 B based on the alert, the informing, the notification, etc. 
     In some examples, in response to determining to renew the request, the SDSi feature management service  256  invokes the SDSi agent management interface  264  to generate an alert, inform, notify, etc., one(s) of the SDSi asset agents  140 A-C that the second SDSi asset agent  140 B is having the certificate(s) renewed. In such examples, the first SDSi asset agent  140 A, the third SDSi agent  140 C, etc., increase an agent reputation score of the second SDSi asset agent  140 B based on the alert, the informing, the notification, etc. 
     In some examples, in response to the SDSi feature management service  256  determining to renew the request, the license processor  214  of the second SDSi asset agent  140 B facilitates provisioning of the license to the second SDSi asset agent  140 B by re-activating the de-activated feature(s). In such examples, in response to a successful reactivation, the agent analytics engine  206  generates a certificate. In some such examples, the agent analytics engine  206  invokes the agent interface  202  to broadcast the renewed certificate(s) to the mesh network  1002 . In some examples, in response to receiving the broadcast, the trusted agent determiner  1104  of the first SDSi asset agent  140 A and the third SDSi asset agent  140 C increase the agent reputation score of the second SDSi asset agent  140 B based on the renewed certificate(s), which is indicative of the second SDSi asset agent  140 B being trustworthy and/or otherwise unlikely to be a rogue or compromised agent. 
     In the illustrated example of  FIG.  11   , the analytics engine  206  includes the anomaly detector  1108  to use artificial intelligence to analyze broadcasts from ones of the SDSi asset agents  140 A-C to detect and/or otherwise identify one or more anomalies. In some examples, the anomaly detector  1108  analyzes the broadcasts from not blocked one(s) of the SDSi asset agents  140 A-C and/or otherwise from one(s) of the SDSi asset agents  140 A-C having relatively high agent reputation scores. In such examples, the anomaly detector  1108  may not analyze anomalies in broadcasts from blocked one(s) of the SDSi asset agents  140 A-C because the trusted agent determiner  1104 , and/or, more generally, a corresponding one of the SDSi asset agents  140 A-C ignores the broadcasts from the blocked one(s) of the SDSi asset agents  140 A-C. 
     In some examples, the anomaly detector  1108  executes one or more AI models, such as the anomaly detection machine learning (ML) model(s)  1110  of the example of  FIG.  11   . In such examples, the anomaly detector  1108  executes the anomaly detection ML model(s)  1110  to analyze trend(s) in a number of issued licenses, a number of activated (or deactivated) features, a value of respective one(s) of the activated (or deactivated) features, etc., and/or a combination thereof. In some such examples, the anomaly detection ML model(s)  1110  output and/or otherwise determine whether differences (e.g., significant differences, substantial differences, etc.) or deviations (e.g., significant deviations, substantial deviations, etc.) are detected based on the AI analysis. In some such examples, the anomaly detector  1108  invokes the trusted agent determiner  1104  to reduce and/or otherwise lower a score for the one of the SDSi asset agents  140 A-C associated with the detected anomalies. 
     In some examples, the analytics engine  206  includes the anomaly detector  1108  to use AI, including ML, deep learning (DL), and/or other artificial machine-driven logic, to enable the analytics engine  206 , and/or, more generally, the SDSi asset agent  140 A-C to use a model, such as the anomaly detection ML model(s)  1110 , to process input data (e.g., certificate data, license data, information included in a broadcast to the mesh network  1002 , etc.) to generate an output (e.g., a detection or identification of one or more anomalies or deviations) based on patterns, trends, and/or associations previously learned by the model via a training process. For instance, the anomaly detector  1108  trains the anomaly detection ML model(s)  1110  with data to recognize patterns, trends, and/or associations and follow such patterns, trends, and/or associations when processing input data such that other input(s) result in output(s) consistent with the recognized patterns, trends, and/or associations. 
     In some examples, the anomaly detector  1108  implements means for detecting an anomaly. For example, the means for detecting executes a machine learning model to detect an anomaly associated with a license, and, in response to a detection of the anomaly, updates a reputation score associated with a second agent of the plurality of the agents based on the anomaly. In this example, the means for detecting is implemented by any processor structured to perform the corresponding operation by executing software or firmware, or hardware circuit (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, a PLD, a FPLD, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate. 
     Many different types of machine learning models and/or machine learning architectures exist. In some examples, a neural network model is used to implement the anomaly detection ML model(s)  1110 . Using a neural network model enables the example anomaly detector  1108  to analyze patterns in certificates, licenses, etc., broadcasted to the mesh network  1002  and identify any anomalies or deviations based on the patterns. In general, ML models/architectures that are suitable to use in the example approaches disclosed herein include recurrent neural networks. However, other types of machine learning models could additionally or alternatively be used such as supervised learning artificial neural network models. Example supervised learning artificial neural network models include two-layer (2-layer) radial basis neural networks (RBN), learning vector quantization (LVQ) classification neural networks, etc. 
     The example anomaly detector  1108  and/or the example anomaly detection ML model(s)  1110  implement(s) an AI/ML system using two phases, a learning/training phase and an inference phase. In the learning/training phase, the anomaly detector  1108  executes a training algorithm to train the anomaly detection ML model(s)  1110  to operate in accordance with patterns, trends, and/or associations based on, for example, training data. In general, the anomaly detection ML model(s)  1110  includes internal parameters that guide how input data is transformed into output data, such as through a series of nodes and connections within the model to transform input data into output data. Additionally, hyperparameters are used as part of the training process to control how the learning is performed (e.g., a learning rate, a number of layers to be used in the machine learning model, etc.). Hyperparameters are defined to be model hyperparameters that are determined prior to initiating the training process. 
     The example anomaly detector  1108  may deploy different types of training based on the type of AI/ML model of the anomaly detection ML model(s)  1110  and/or the expected output. In some examples, the anomaly detector  1108  deploys supervised training to use inputs and corresponding expected (e.g., labeled) outputs to select parameters (e.g., by iterating over combinations of select parameters) for the anomaly detection ML model(s)  1110  to reduce model error. As used herein, labelling refers to an expected output of the anomaly detection ML model(s)  1110  (e.g., a classification, an expected output value, etc.). In some examples, the anomaly detector  1108  deploys unsupervised training (e.g., used in deep learning, a subset of machine learning, etc.) to use inferring patterns from inputs to select parameters for the anomaly detection ML model(s)  1110  (e.g., without the benefit of expected (e.g., labeled) outputs). 
     In some examples, the anomaly detector  1108  trains the anomaly detection ML model(s)  1110  using unsupervised learning. In some examples, the anomaly detector  1108  trains the anomaly detection ML model(s)  1110  using stochastic gradient descent. However, any other training algorithm may additionally or alternatively be used. In some examples, the anomaly detector  1108  can perform training of the anomaly detection ML model(s)  1110  until the level of error is no longer reducing. In some examples, the anomaly detector  1108  executes the training by performing the training locally on the semiconductor device  105 A-C. In some examples, the training is performed remotely at an external computing system (e.g., the manufacturer enterprise system  110 , the customer enterprise system  115 , etc.) communicatively coupled to the semiconductor device  105 A-C. 
     In some examples, the anomaly detector  1108  performs the training of the anomaly detection ML model(s)  1110  using hyperparameters that control how the learning is performed (e.g., a learning rate, a number of layers to be used in the anomaly detection ML model(s)  1110 , etc.). In some examples, hyperparameters that control model performance and training speed are the learning rate and regularization parameter(s). Such hyperparameters are selected by, for example, trial and error, a customer associated with the customer enterprise system  115  based on one or more requirements or specifications, etc., to reach an optimal model performance. In some examples, the anomaly detector  1108  utilizes Bayesian hyperparameter optimization to determine an optimal and/or otherwise improved or more efficient network architecture to avoid model overfitting and improve model&#39;s overall applicability. In some examples, the anomaly detector  1108  determines that re-training of the anomaly detection ML model(s)  1110  is to be performed. In such examples, the anomaly detector  1108  determines to execute such re-training in response to a predetermined time period elapsing, a quantity of certificate data, license data, etc., obtained from the mesh network  1002 , a receipt of a re-training request by a user or external computing system, etc., and/or a combination thereof. 
     Training is performed using training data. In some examples, the training data originates from locally generated data, such as telemetry data from the SDSi asset agents  140 A-C. In some examples where supervised training is used, the training data is labeled. Labeling is applied to the training data by a user manually or by an automated data pre-processing system. In some examples, the training data is pre-processed using, for example, an interface (e.g., the agent interface  202 ), or other portion of the SDSi asset agent  140 A-C, such as the trusted agent determiner  1104 , the certificate validator  1106 , the anomaly detector  1108 , etc., to determine training data. In some examples, the anomaly detector  1108  sub-divides the training data into two or more portions, such as a first portion of data for training the model and a second portion of data for validating the model. 
     Once training is complete, the example anomaly detector  1108  deploys the anomaly detection ML model(s)  1110  for use as an executable construct that processes an input and provides an output based on the network of nodes and connections defined in the anomaly detection ML model(s)  1110 . The anomaly detection ML model(s)  1110  is stored in memory of the SDSi asset agent  140 A-C, the semiconductor device  105 A-C, etc., or in a database of a remote computing system, such as one or more servers associated with at least one of the manufacturer enterprise system  110  or the customer enterprise system  115 . The example anomaly detector  1108  may then execute the example anomaly detection ML model(s)  1110  to detect anomalies associated with broadcast(s) from one(s) of the example SDSi asset agents  140 A-C. 
     Once trained, the example deployed anomaly detection ML model(s)  1110  may be operated in an inference phase to process data. In the inference phase, data to be analyzed (e.g., live data) is input to the example anomaly detection ML model(s)  1110 , and the anomaly detection ML model(s)  1110  execute(s) to create an output. By way of example, this inference phase can be thought of as the AI “thinking” to generate the output based on what it learned from the training (e.g., by executing the model to apply the learned patterns and/or associations to the live data). In some examples, input data undergoes pre-processing before being used as an input to the anomaly detection ML model(s)  1110 . Moreover, in some examples, the output data may undergo post-processing after it is generated by the anomaly detection ML model(s)  1110  to transform the output into a useful result (e.g., a display of data, an instruction to be executed by a machine, etc.). 
     In some examples, output(s) of the deployed anomaly detection ML model(s)  1110  may be captured and provided as feedback. By analyzing the feedback, an accuracy of the deployed model can be determined. If the feedback indicates that the accuracy of the deployed model is less than a threshold or other criterion, the example anomaly detector  1108  triggers training of an updated model using the feedback and an updated training data set, hyperparameters, etc., to generate an updated, deployed one(s) of the example anomaly detection ML model(s)  1110 . 
     In the illustrated example of  FIG.  11   , the hardware circuitry  125 , the firmware  130 , and/or the BIOS  135  implement an example feature intent determiner  1112  and example feature intent machine learning (ML) model(s)  1112 . In some examples, the feature intent determiner  1112  determine an intent or expected outcome of a request to change a hardware asset, such as the semiconductor devices  105 A-C, post manufacture. Typically, tools provided by silicon product manufacturers require an operator, a user, etc., to know details about features that need to activated or deactivated. Such a request to change the hardware asset may be in response to a customer order for manufacturing a server platform, an increased workload caused by a peak in computing and/or network traffic experienced by a customer associated with the customer enterprise system  115 , etc. Such examples of bases for the request may require deep knowledge of the workload and understanding of the available hardware, software, and/or firmware features of the semiconductor devices  105 A-C. In some such examples, the capability of changing a configuration of an asset by a customer may not be fully leveraged, which may lead to suboptimal asset utilization and/or increased costs. In some examples, a specific combination of features may result in better performance while another combination of other features may significantly decrease performance of execution. In such examples, the complexity of such dependencies increases with subsequent generations of semiconductor devices because of new features being introduced or existing features being modified. 
     Advantageously, the example feature intent determiner  1112  reduces the complexity of understanding features of the semiconductor device  105 A-C when requesting a configuration change to improve performance of execution of computing workloads. For example, a configuration change of the semiconductor device  105 A-C may include an activation of one or more first features, an activation of one or more second features, a deactivation of one or more third features, and/or a deactivation of one or more fourth features. In some examples, the feature intent determiner  1112  obtains a request for a configuration change of one(s) of the semiconductor devices  105 A-C. In such examples, the request is based on and/or otherwise formatted according to a high-level meta-language to enable a requester (e.g., a user, a computing device, etc.) to define an expected outcome of the configuration change. 
     In some examples, the feature intent determiner  1112  deploys a language parser (e.g., a language parsing engine) to translate the request for the configuration change into one or more requirements associated with availability, machine learning, performance, reliability, security, etc., and translate the one or more requirements into one or more features of the semiconductor device  105 A-C that, when activated, deliver the configuration change in accordance with the expected outcome. In such examples, the feature intent determiner  1112  invokes execution of the feature intent ML model(s)  1114  to determine whether to adjust and/or otherwise modify the one or more features identified by the feature intent determiner  1112 . In some examples, in response to an identification of the one or more features, the modified one(s) of the one or more features, etc., the feature intent determiner  1112  invokes the license processor  214  to obtain (e.g., automatically obtain) the corresponding license(s) for the identified feature(s). 
     In some examples, the feature intent ML model(s)  1114  obtain(s) data (e.g., training data) from the mesh network  1002 , the manufacturer enterprise network  110 , and/or the customer enterprise network  115 . Such example data includes system diagnostics and/or information associated with workloads previously executed, currently being executed, and/or in a queue to be processed by one(s) of the semiconductor devices  105 A-C. Advantageously, the example feature intent determiner  1112  and/or the example feature intent ML model(s)  1114  implement a self-learning AI/ML system to adaptively handle and/or otherwise execute dynamically changing workloads with optimum and/or otherwise improved performance. 
     In some examples, the hardware circuitry  125 , the firmware  130 , and/or the BIOS  135  include the feature intent determiner  1112  to use AI, including ML, DL, and/or other artificial machine-driven logic, to use a model, such as the feature intent ML model(s)  1114 , to process input data (e.g., system diagnostics, workload data, requested features, requirements, etc.) to generate an output (e.g., one or more features to be activated (or deactivated) based on patterns, trends, and/or associations previously learned by the feature intent ML model(s)  1114  via a training process. For instance, the feature intent determiner  1112  trains the feature intent ML model(s)  1114  with data to recognize patterns, trends, and/or associations and follow such patterns, trends, and/or associations when processing input data such that other input(s) result in output(s) consistent with the recognized patterns, trends, and/or associations. 
     In some examples, a neural network model is used to implement the feature intent ML model(s)  1114 . Using a neural network model enables the example feature intent determiner  1112  to analyze patterns in system diagnostics, workloads, requested features, etc., broadcasted to the mesh network  1002  and/or stored by at least one of the manufacturer enterprise system  110  or the customer enterprise system  115 . In some examples, other types of machine learning models could additionally or alternatively be used to implement the feature intent ML model(s)  1114 , such as supervised learning artificial neural network models. 
     The example feature intent determiner  1112  and/or the example feature intent ML model(s)  1114  implement(s) an AI/ML system using two phases, a learning/training phase and an inference phase. In the learning/training phase, the example feature intent determiner  1112  executes a training algorithm to train the example feature intent ML model(s)  1114  to operate in accordance with patterns, trends, and/or associations based on, for example, training data. In general, the feature intent ML model(s)  1114  include(s) internal parameters that guide how input data is transformed into output data, such as through a series of nodes and connections within the model to transform input data into output data. Additionally, hyperparameters are used as part of the training process to control how the learning is performed (e.g., a learning rate, a number of layers to be used in the machine learning model, etc.). Hyperparameters are defined to be model hyperparameters that are determined prior to initiating the training process. 
     The example feature intent determiner  1112  may deploy different types of training based on the type of AI/ML model of the feature intent ML model(s)  1114  and/or the expected output. In some examples, the feature intent determiner  1112  deploys supervised training to use inputs and corresponding expected (e.g., labeled) outputs to select parameters (e.g., by iterating over combinations of select parameters) for the feature intent ML model(s)  1114  to reduce model error. As used herein, labelling refers to an expected output of the feature intent ML model(s)  1114  (e.g., a classification, an expected output value, etc.). In some examples, the feature intent determiner  1112  deploys unsupervised training (e.g., used in deep learning, a subset of machine learning, etc.) to use inferring patterns from inputs to select parameters for the feature intent ML model(s)  1114  (e.g., without the benefit of expected (e.g., labeled) outputs). 
     In some examples, the feature intent determiner  1112  trains the feature intent ML model(s)  1114  using unsupervised learning. In some examples, the feature intent determiner  1112  trains the feature intent ML model(s)  1114  using stochastic gradient descent. However, any other training algorithm may additionally or alternatively be used. In some examples, the feature intent determiner  1112  can perform training of the feature intent ML model(s)  1114  until the level of error is no longer reducing. In some examples, the feature intent determiner  1112  executes the training by performing the training locally on the semiconductor device  105 A-C. In some examples, the training is performed remotely at an external computing system (e.g., the manufacturer enterprise system  110 , the customer enterprise system  115 , etc.) communicatively coupled to the semiconductor device  105 A-C. 
     In some examples, the feature intent determiner  1112  performs the training of the feature intent ML model(s)  1114  using hyperparameters that control how the learning is performed (e.g., a learning rate, a number of layers to be used in the feature intent ML model(s)  1114 , etc.). In some examples, hyperparameters that control model performance and training speed are the learning rate and regularization parameter(s). Such hyperparameters are selected by, for example, trial and error, a customer associated with the customer enterprise system  115  based on one or more requirements or specifications, etc., to reach an optimal model performance. In some examples, the feature intent determiner  1112  utilizes Bayesian hyperparameter optimization to determine an optimal and/or otherwise improved or more efficient network architecture to avoid model overfitting and improve model&#39;s overall applicability. In some examples, the feature intent determiner  1112  determines that re-training of the feature intent ML model(s)  1114  is to be performed. In such examples, the feature intent determiner  1112  determines to execute such re-training in response to a predetermined time period elapsing, a quantity of system diagnostics, workload data, etc., obtained from the mesh network  1002 , a receipt of a re-training request by a user or external computing system, etc., and/or a combination thereof. 
     Training is performed using training data. In some examples, the training data originates from locally generated data, such as telemetry data from the SDSi asset agents  140 A-C, the system diagnostics, the workload data, etc. In some examples where supervised training is used, the training data is labeled. Labeling is applied to the training data by a user manually or by an automated data pre-processing system. In some examples, the training data is pre-processed using, for example, an interface (e.g., the agent interface  202 ) or other portion of the SDSi asset agent  140 A-C to determine training data. In some examples, the feature intent determiner  1112  sub-divides the training data into two or more portions, such as a first portion of data for training the model and a second portion of data for validating the model. 
     Once training is complete, the example feature intent determiner  1112  deploys the feature intent ML model(s)  1114  for use as an executable construct that processes an input and provides an output based on the network of nodes and connections defined in the feature intent ML model(s)  1114 . The example feature intent ML model(s)  1114  is stored in memory of the semiconductor device  105 A-C, etc., or in a database of a remote computing system, such as one or more servers associated with at least one of the example manufacturer enterprise system  110  or the example customer enterprise system  115 . The example feature intent determiner  1112  may then execute the example feature intent ML model(s)  1114  to determine one or more features to activate based on an intent or expected outcome from a request to change a configuration of one(s) of the example semiconductor devices  105 A-C. 
     Once trained, the deployed feature intent ML model(s)  1114  may be operated in an inference phase to process data. In the inference phase, data to be analyzed (e.g., live data) is input to the example feature intent ML model(s)  1114 , and the feature intent model(s)  1114  execute(s) to create an output. In some examples, input data undergoes pre-processing before being used as an input to the feature intent ML model(s)  1114 . Moreover, in some examples, the output data may undergo post-processing after it is generated by the feature intent ML model(s)  1114  to transform the output into a useful result (e.g., a display of data, an instruction to be executed by a machine, etc.). 
     In some examples, output(s) of the deployed feature intent ML model(s)  1114  may be captured and provided as feedback. By analyzing the feedback, an accuracy of the deployed model can be determined. If the feedback indicates that the accuracy of the deployed model is less than a threshold or other criterion, the example feature intent determiner  1112  triggers training of an updated model using the feedback and an updated training data set, hyperparameters, etc., to generate an updated, deployed one(s) of the example feature intent ML model(s)  1114 . 
       FIG.  12    depicts a block diagram of an example system  1200  that illustrates example implementations of the SDSi asset agent  140  and/or one(s) of the SDSi asset agents  140 A-C, the manufacturer enterprise system  110 , and the customer enterprise system  115  included in the example system  100  of  FIG.  1   , the example system  1000  of  FIG.  10   , and/or the example system  1100  of  FIG.  11   . In the illustrated example of  FIG.  12   , the system  1200  includes the example SDSi portal  262  and the example SDSi agent management interface  264  of  FIG.  2   . In the example of  FIG.  12   , the example SDSi portal  262  and the example SDSi agent management interface  264  are implemented as cloud services in the cloud platform  120 . In the illustrated example of  FIG.  12   , the example customer enterprise system  115  of  FIG.  2   ,  FIG.  10   , and/or  FIG.  11    includes the example SDSi client agent  272 , the example platform inventory management service  274 , and the example entitlement management service  278  of  FIG.  2   . Additionally or alternatively, the example customer enterprise system  115  of  FIG.  12    may include the example accounts management service  276  of the example of  FIG.  2   . 
     In the illustrated example of  FIG.  12   , the system  1200  includes an example implementation of the SDSi asset agent  140  of  FIG.  1   , and/or more generally, the semiconductor device  105  of  FIG.  1   , and/or an example implementation of the SDSi asset agents  140 A-C of  FIG.  10   , and/or, more generally, the semiconductor devices  105 A-C of  FIG.  10   . In the illustrated example of  FIG.  12   , the SDSi asset agents  140 A-C include the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication service(s)  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218 , and the example feature libraries  220 - 230  of  FIG.  2    corresponding to the respective example feature sets  232 - 242  of FIG.  2  implemented by the example hardware circuitry  125 , the example firmware  130 , and/or the example BIOS  135  of  FIG.  2   . 
     In the illustrated example of  FIG.  12   , the agent daemon  212  includes an example trusted execution environment (TEE) identifier  1202 , an example TEE generator  1204 , and example TEE(s)  1205 . In the illustrated example of  FIG.  12   , the agent library  218  includes an example TEE library  1206 . In the illustrated example of  FIG.  12   , the hardware circuitry  125 , the firmware  130 , and/or the BIOS  135  include example TEE component(s)  1208 , the feature intent determiner  1112  of  FIG.  11   , and the feature intent ML model(s)  1114  of  FIG.  11   . 
     The example agent daemon  212  securely executes the elements of the SDSi asset agent  140 A-C. For example, the agent daemon  212  executes one or more of the agent interface  202 , the agent local services  204 , the analytics engine  206 , the communication services  208 , the agent CLI  210 , and/or the license processor  214  in a protected environment, such as one(s) of the trusted execution environment(s) (TEE(s))  1205 , implemented by the semiconductor device  105 A-C. The SDSi asset agent  140 A-C of the illustrated example includes the agent library  218  to provide, among other things, hardware-agnostic application programming interfaces (APIs), such as TEE APIs included in the TEE library  1206 . 
     In some examples, the agent daemon  212  invokes the TEE generator  1204  to generate an execution environment, such as one(s) of the TEE(s)  1205 , that allows an application to run in a resource envelope that has the necessary TEE component(s)  1208 , such as trusted execution, trusted memory, trusted storage, etc., to protect application specific secrets or other data in compliance with the highest certifications. Prior security technologies must be purpose built to leverage a TEE and must therefore be deployed into an end user environment as a combination of hardware and software that greatly increases the cost and complexity of the overall security solution. Advantageously, the example agent daemon  212  enables an application (e.g., software) to operate at two or more levels of intelligence and allow an application architecture that is not coupled (e.g., tightly coupled, integrated, etc.) to a particular TEE technology from a hardware perspective. In some examples, the agent daemon  212 , at a first level of intelligence, can allow an instance of the application to leverage and/or otherwise take advantage of an available TEE identified by the TEE identifier  1202  to which the application has access, either local or remote to the semiconductor device  105 A-C. In some examples, the agent daemon  214 , at a second level of intelligence, can invoke the TEE generator  1204  to compose, assemble, compile, and/or otherwise generate the TEE(s)  1205  from one or more of the TEE component(s)  1208  to meet desired security requirements. In such examples, the agent daemon  214  invokes the TEE generator  1204  to generate the TEE(s)  1205  in response to a determination that a standard or known TEE is not available or leverage a software-based TEE definable by the TEE library  1206 . 
     In the illustrated example of  FIG.  12   , the agent daemon  212  includes the TEE identifier  1202  to explore an environment (e.g., an execution environment) of a semiconductor device, such as the semiconductor devices  105 A-C, for known TEE(s) based on security requirements. For example, the TEE(s)  1205  include one or more known or conventional TEEs. In some examples, the TEE identifier  1202  obtains a request to deploy a TEE, such as one(s) of the TEE(s)  1205 , on the first semiconductor device  105 A based on security requirements including a request for trusted execution, trusted memory, trusted storage, trusted key derivation and management, etc. In such examples, the TEE identifier  1202  identifies whether the first semiconductor device  105 A supports one or more known TEEs that comply with the security requirements. In some such examples, the TEE identifier  1202  maps the security requirements to the TEE(s)  1205 , the TEE library  1206 , one or more hardware-based TEEs deployable by one(s) of the features  232 ,  234 ,  236 ,  238 ,  240 ,  242 ,  1208 , etc. For example, the TEE identifier  1202  identifies that the first semiconductor device  105 A has one or more known TEEs based on mapping the security requirements to the one or more known TEEs that comply with the security requirements. 
     Advantageously, the example TEE identifier  1202  senses an environment of the SDSi asset agent  140 A-C, and/or, more generally, the semiconductor device  105 A-C, for a presence of one or more TEEs, such as one(s) of the TEE(s)  1205 , and selects from amongst the detected one or more TEEs that is/are best suited to protect the application, data associated with the application, and/or portion(s) thereof. Advantageously, in some examples, the TEE identifier  1202  selects one or more APIs from the TEE library  1206  that correspond to the selected TEE and configures the one or more selected APIs to generate an abstraction layer for the selected TEE. In such examples, the one or more configured APIs enable the application to interface with the selected TEE without concerning itself with the API specifics of the selected TEE. 
     In some examples, the TEE identifier  1202  implements means for identifying whether a semiconductor device supports a first TEE based on security requirements. In such examples, the semiconductor device includes circuitry configurable to provide one or more features. In this example, the means for identifying is implemented by any processor structured to perform the corresponding operation by executing software or firmware, or hardware circuit (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, a PLD, a FPLD, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate. 
     In some examples, in response to identifying the one or more known TEEs, the TEE generator  1204  selects one of the one or more known TEEs to deploy. For example, in response to identifying one of the TEE(s)  1205  as a known TEE, the TEE generator  1204  deploys the known TEE to protect application data, cryptographic data, etc., of interest. In such examples, the TEE generator  1204  invokes an application to execute in the deployed TEE to protect the secrets or other data associated with the application. In some examples, in response to not identifying a known TEE that complies with requested security requirements, the TEE generator  1204  generates a TEE based on a software-based TEE definable by the TEE library  1206  and/or the TEE component(s)  1208  either local or remote to the semiconductor device  105 A-C. 
     In the illustrated example of  FIG.  12   , the agent daemon  212  includes the TEE generator  1204  to explore an environment of the SDSi asset agent  140 A-C, and/or, more generally, the semiconductor device  105 A-C, for a presence of one or more of the TEE components  1208 . In some examples, the TEE generator  1204  determines whether a TEE (e.g., a hardware-based TEE) is composable based on identified one(s) of the TEE component(s)  1208  and/or security requirements included in a TEE deployment request. In some such examples, based on the determination that the TEE is composable, the TEE generator  1204  deploys the TEE, such as one(s) of the TEE(s)  1205  which, in some examples, is/are hardware-based TEE(s), based on at least one of the trusted execution, the trusted memory, or the trusted storage included in the TEE component(s)  1208 . In some such examples, based on the determination that the TEE is not composable, the TEE generator  1204  deploys a different type of TEE, such as a software-based TEE included in the TEE library  1206 , based on the security requirements. 
     In some examples, the TEE generator  1204  generates a handler (e.g., a TEE handler). For example, the TEE generator  1204  generates a handler to implement routine(s) (e.g., firmware and/or software routine(s)), function(s) (e.g., software and/or firmware function(s)), method(s) (e.g., firmware and/or software method(s)), etc., and/or a combination thereof, to expose a set of capabilities to the SDSi asset agent  140 A-C to effectuate TEE protection for the data, the processes, etc., of an application that the SDSi asset agent  140 A-C is tasked to protect. In such examples, in response to deploying a TEE, the TEE generator  1204  returns the handler or an abstracted instance of the deployed TEE that is configured to receive calls from the SDSi asset agent  140 A-C to protect the data, the processes, etc., of an application of interest. 
     In some examples, the TEE generator  1204  implements means for generating a second TEE based on one or more features in response to a determination to generate the second TEE based on an identification whether a semiconductor device supports a first TEE based on security requirements. In some examples, the means for generating generates the second TEE in response to the determination indicating that the first TEE is not supported by the semiconductor device. In some examples, the means for generating determines whether the second TEE is deployable as a hardware TEE based on the one or more features, and deploys the second TEE as the hardware TEE based on the determination. In some examples, the means for generating deploys the second TEE as a software TEE in response to the determination indicating that the second TEE is not deployable as the hardware TEE. In this example, the means for generating is implemented by any processor structured to perform the corresponding operation by executing software or firmware, or hardware circuit (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, a PLD, a FPLD, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate. 
     In some examples, one or more of the TEE identifier  1202 , the TEE generator  1204 , the TEE(s),  1205 , and/or the TEE library  1206  are deployed to the SDSi asset agent  140 A-C. For example, in response to one or more licenses being issued to the first SDSi asset agent  140 A, at least one of the TEE identifier  1202 , the TEE generator  1204 , the TEE(s)  1205 , or the TEE library  1206  is/are deployed to the SDSi asset agent  140 A-C. 
     In the illustrated example of  FIG.  12   , the agent daemon  212  includes the TEE(s)  1205  to securely execute the elements of the SDSi asset agent  140 A-C. In some examples, the TEE(s)  1205  include one or more TEEs. For example, the TEE(s)  1205  include one or more known or conventional TEEs obtained from an external computing system, such as the manufacturer enterprise system  110  and/or the customer enterprise system  115 . In some examples, the TEE(s)  1205  include one or more different types of TEEs. For example, the TEE(s)  1205  include one or more firmware-based TEEs, one or more software-based TEEs, and/or one or more hardware-based TEEs. For example, the TEE(s)  1205  include one or more firmware and/or software-based TEEs that can be exposed to an element of the SDSi asset agent  140 A-C, the manufacturer enterprise system  110 , and/or the customer enterprise system  115  via hardware-agnostic application programming interfaces (APIs), such as TEE APIs included in the TEE library  1206 . In other examples, the TEE(s)  1205  include one or more hardware-based TEEs generated from one(s) of the features  232 ,  234 ,  236 ,  238 ,  240 ,  242 ,  1208 . In such examples, the one or more hardware-based TEEs can be exposed to an element of the SDSi asset agent  140 A-C, the manufacturer enterprise system  110 , and/or the customer enterprise system  115  via hardware-agnostic application programming interfaces (APIs), such as TEE APIs included in the TEE library  1206 . Although the TEE(s)  1205  are depicted as being included in the agent daemon  212 , additionally or alternatively, the TEE(s)  1205  may be included in one or more different elements of the SDSi asset agent  140 A-C, the hardware circuitry  125 , the firmware  130 , and/or the BIOS  135  of the semiconductor device  105 A-C. For example, one or more of the TEEs  1205  are included in at least one of the agent daemon  212 , the hardware circuitry  125 , the firmware  130 , or the BIOS  135  of the semiconductor device  105 A-C. 
     In the illustrated example of  FIG.  12   , the agent library  218  includes the TEE library  1206  to store a suite of data and/or machine readable instructions (e.g., API(s), programming code, software handler(s), etc.). In some examples, the data and/or the machine readable instructions are configured to expose an API used to scan an environment (e.g., a compute environment) for the presence of TEEs, the initialization of the TEEs, and/or the use or execution of the TEEs. In some examples, the TEE library  1206  includes one or more known TEEs (e.g., one or more known software or software-based TEEs, one or more known hardware or hardware-based TEEs that can be composed from the TEE component(s)  1208 , etc., and/or a combination thereof), one or more TEE APIs (e.g., one or more known TEE APIs that correspond to the one or more known TEEs), etc., and/or a combination thereof. 
     In the illustrated example of  FIG.  12   , the features include the TEE component(s)  1208  to be used to assemble, compose, and/or otherwise generate a TEE, such as one(s) of the TEE(s)  1205  (e.g., one or more hardware-based TEEs, one or more firmware-based TEEs, one or more software-based TEEs, etc., and/or a combination thereof). In some examples, the TEE components  308  include one or more hardware TEE components, such as secure or trusted compute resources (e.g., one or more cores of a multi-core CPU), memory, storage, bus(es), peripheral(s), etc. In some examples, the TEE components  308  include one or more firmware and/or BIOS TEE components, such as a secure or trusted bootloader, kernel, filesystem, trusted application, interrupt, etc. For example, the TEE generator  1204  assembles one(s) of the TEE component(s)  1208  and deploys the assembly, compilation, etc., as the TEE(s)  1205  in the agent daemon  212 , as the TEE(s)  1205  in the hardware circuitry  125 , as the TEE(s)  1205  in the firmware  130 , and/or as the TEE(s)  1205  in the BIOS  135  of the semiconductor device  105 A-C. 
     The SDSi asset agent  140 A-C of the illustrated example includes the feature intent determiner  1112  and the feature intent ML model(s)  1114  to translate an intent or expected outcome from a request to deploy the TEE(s)  1205 . In some examples, in response to a request to improve security of the system  1200 , the feature intent determiner  1112  translates the request using a high-level meta-language (e.g., C, C++, Java, C#, Perl, Python, HyperText Markup Language (HTML), Structured Query Language (SQL), Swift, etc.) into one or more security requirements, one or more TEE requirements, etc. In such examples, the feature intent determiner  1112  invokes the TEE identifier  1202  to identify one or more known TEEs supported by the semiconductor device  105 A-C, one or more TEE component(s)  1208  of the semiconductor device  105 A-C, etc. In some such examples, the feature intent determiner  1112  invokes the one or more feature intent ML models  1114  to output a TEE configuration to meet and/or otherwise satisfy the intent of the request. For example, the one or more feature intent ML models  1114  determine whether one or more known TEEs supported by the semiconductor device  105 A-C meet and/or otherwise satisfy the intended outcome of the request to improve security of the system  1200 . In other examples, the one or more feature intent ML models  1114  determine whether one or more TEEs are composable based on the TEE library  1206 , the TEE component(s)  1208 , etc., to meet and/or otherwise satisfy the intended outcome of the request to improve security of the system  1200 . Advantageously, the example TEE generator  1204  composes a TEE, such as the TEE(s)  1205 , based on the output(s) from the feature intent ML model(s)  1114  to optimize and/or otherwise improve security of the system  1200 . 
     In some examples, the feature intent determiner  1112  implements means for determining a feature intent. For example, the means for determining obtains a request to deploy a TEE, translates an intent of the request to the security requirements, executes a machine learning model to determine the one or more features, and generates the second TEE based on the one or more features. In such examples, the one or more features are a first set of features, and the means for determining determines to adjust the first set of features into a second set of one or more features, and re-trains the machine learning model based on the second set of the one or more features. In this example, the means for determining is implemented by any processor structured to perform the corresponding operation by executing software or firmware, or hardware circuit (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, a PLD, a FPLD, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate. 
     In some examples, the agent interface  202  implements means for interfacing with an agent. For example, the means for interfacing determines whether the one or more features of the semiconductor device includes a first feature representative of translating the intent, and in response to determining that the one or more features do not include the first feature, activates the first feature. In this example, the means for interfacing is implemented by any processor structured to perform the corresponding operation by executing software or firmware, or hardware circuit (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, a PLD, a FPLD, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware, but other structures are likewise appropriate. 
     While example manners of implementing the systems  100 ,  200 ,  1000 ,  1100 , and/or  1200  are illustrated in  FIGS.  1 - 9  and  10 - 12   , one or more of the elements, processes and/or devices illustrated in  FIGS.  1 - 9  and  10 - 12    may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example silicon product  105  (e.g., the example semiconductor device  105 ), the example manufacturer enterprise system  110 , the example customer enterprise system  115 , the example cloud platform  120 , the example SDSi asset agent  140 , the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication services  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218 , the example feature libraries  220 - 230 , the example product management service  252 , the example customer management service  254 , the example SDSi feature management service  256 , the example SDSi portal  262 , the example SDSi agent management interface  264 , the example manufacturer trusted agent determiner  1102 , the example SDSi client agent  272 , the example platform inventory management service  274 , the example accounts management service  276 , the example entitlement management service  278 , the example trusted agent determiner  1104 , the example certificate validator  1106 , the example anomaly detector  1108 , the example anomaly detection ML model(s)  1110 , the example feature intent determiner  1112 , the example feature intent ML model(s)  1114 , the example TEE identifier  1202 , the example TEE generator  1204 , the example TEE(s)  1205 , the example TEE library  1206 , the example TEE component(s)  1208 , and/or, more generally, the systems  100 ,  200 ,  1000 ,  1100 , and/or  1200  of  FIGS.  1 - 9  and  10 - 12    may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example silicon product  105  (e.g., the example semiconductor device  105 ), the example manufacturer enterprise system  110 , the example customer enterprise system  115 , the example cloud platform  120 , the example SDSi asset agent  140 , the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication services  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218 , the example feature libraries  220 - 230 , the example product management service  252 , the example customer management service  254 , the example SDSi feature management service  256 , the example SDSi portal  262 , the example SDSi agent management interface  264 , the example manufacturer trusted agent determiner  1102 , the example SDSi client agent  272 , the example platform inventory management service  274 , the example accounts management service  276 , the example entitlement management service  278 , the example trusted agent determiner  1104 , the example certificate validator  1106 , the example anomaly detector  1108 , the example anomaly detection ML model(s)  1110 , the example feature intent determiner  1112 , the example feature intent ML model(s)  1114 , the example TEE identifier  1202 , the example TEE generator  1204 , the example TEE(s)  1205 , the example TEE library  1206 , the example TEE component(s)  1208 , and/or, more generally, the systems  100 ,  200 ,  1000 ,  1100 , and/or  1200  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), programmable controller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)), field programmable gate arrays (FPGAs) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example systems  100 ,  200 ,  1000 ,  1100 ,  1200 , the example silicon product  105  (e.g., the example semiconductor device  105 ), the example manufacturer enterprise system  110 , the example customer enterprise system  115 , the example cloud platform  120 , the example SDSi asset agent  140 , the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication services  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218 , the example feature libraries  220 - 230 , the example product management service  252 , the example customer management service  254 , the example SDSi feature management service  256 , the example SDSi portal  262 , the example SDSi agent management interface  264 , the example manufacturer trusted agent determiner  1102 , the example SDSi client agent  272 , the example platform inventory management service  274 , the example accounts management service  276 , the example entitlement management service  278 , the example trusted agent determiner  1104 , the example certificate validator  1106 , the example anomaly detector  1108 , the example anomaly detection ML model(s)  1110 , the example feature intent determiner  1112 , the example feature intent ML model(s)  1114 , the example TEE identifier  1202 , the example TEE generator  1204 , the example TEE(s)  1205 , the example TEE library  1206 , and/or the example TEE component(s)  1208  is/are hereby expressly defined to include a non-transitory computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. including the software and/or firmware. Further still, the example systems  100 ,  200 ,  1000 ,  1100 , and/or  1200  may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIGS.  1 - 9  and  10 - 12   , and/or may include more than one of any or all of the illustrated elements, processes and devices. As used herein, the phrase “in communication,” including variations thereof, encompasses direct communication and/or indirect communication through one or more intermediary components, and does not require direct physical (e.g., wired) communication and/or constant communication, but rather additionally includes selective communication at periodic intervals, scheduled intervals, aperiodic intervals, and/or one-time events. 
     Device Enhancements 
     A block diagram of another example system  1300  to implement and manage SDSi products in accordance with the teachings of this disclosure is illustrated in  FIG.  13   .  FIG.  13    is a block diagram of another example system  1300  to implement and manage an example software defined silicon product  1305  in accordance with the teachings of this disclosure. The example SDSi system  1300  and the example silicon product  1305  of  FIG.  13    includes some of the features and/or elements of the example silicon product  105  of  FIG.  1   . Particularly, the example SDSi system  1300  includes the example SDSi agent  140 , the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication services  208 , the example agent command line interface (CLI)  210 , the example agent daemon  212 , the example license processor  214 , and the example agent library  218 , the example feature libraries  220 - 230 , and the respective example feature sets  232 - 242 . Unless stated otherwise, the features of  FIG.  2    included in  FIG.  13    have the same function, form and relationships as described in connection with these features above. 
     The example SDSi system  1300  of  FIG.  13    additionally includes an example time calculator  1302  and an example feature group calculator  1304 . In the illustrated example of  FIG.  13   , the example SDSI system  1300  further includes example time-dependent features  1306  and sensor features  1308 , which may be implemented by the hardware circuitry  125 , firmware  130 , and/or BIOS  135  of the silicon product  1305 . 
     The example time calculator  1302  determines the absolute time and/or relative time when queried. In the illustrated example of  FIG.  13   , the time calculator  1302  determines the absolute time and/or relative based on the electrical properties of the time-dependent feature  1306 . For example, the time calculator  1302  can determine the relative time based on a difference in the electrical properties of the time-dependent feature  1306  at a time of manufacture of the silicon product  1305  and the current electrical properties. The example time calculator  1302  can determine the absolute time based on the determined relative time and the recorded time of manufacture, which can also be stored and/or determined when the silicon product  1305  is manufactured. In such examples, the time calculator  1302  can determine and/or store the electric properties of the time-dependent feature  1306  when the silicon product is manufactured. An example implementation of the time calculator  1302  is described in greater detail below in conjunction with  FIG.  14   . 
     The example feature group calculator  1304  determines if configurations associated with the silicon product are permissible (e.g., do not exceed the operational capacity of the silicon product, etc.). For example, the feature group calculator  1304 , in response to receiving a new feature combination, can perform a calculation to determine if the configuration associated with this feature combination of the CPU is permissible. In some examples, the feature group calculator  1304  determines the features associated with a license, determines values (e.g., scores, weights, etc.) associated with each of those features and then calculates a group score based on the values. In some examples, the feature group calculator  1304  compares the determined group score to one of more thresholds. For example, the feature group calculator  1304  can compare the calculated group score to a warranty threshold and/or a disable threshold. In some examples, if the feature group calculator  1304  determines the calculate group score exceeds the warranty threshold, the feature group calculator  1304  can void the warranty of the silicon product. In some examples, if the feature group calculator  1304  determines the calculated group score exceeds the disable threshold, the feature group calculator  1304  can prevent the license associated with the new feature combination from being activated. In some examples, the feature group calculator  1304  can include environmental factors (e.g., as detected/determined by the sensor features  1308 , etc.) in the group score calculation(s). An example implementation of the feature group calculator  1304  is described below in conjunction with  FIG.  15   . 
     The example time-dependent feature  1306  is a mechanical (e.g., physical, etc.) feature embedded, installed and/or otherwise coupled to the silicon product  1305 . In the illustrated example of  FIG.  13   , the time-dependent feature  1306  has one or more electrical propertie(s) (e.g., resistance, capacitance, inductance, etc.) that change over time in a predictable manner. Particularly, the time-dependent feature  1306  has one or more electrical parameters and/or properties that are primarily a function of time. As such, if the electrical property of the time-dependent feature  1306  is checked at a first time (e.g., a time of manufacture, etc.), the time calculator  1302  can determine a relative time difference between the first time and a second time, based on the difference in the measured electric property at the first time and the second time. As such, the time-dependent feature  1306  enables an interested party (e.g., the manufacturer enterprise system  110 , the customer enterprise system  105 , etc.) to determine absolute and relative time references without relying on the silicon product  1305  being powered. In some examples, the time-dependent feature  1306  can be implemented by a radioisotope with a relative long half-life (e.g., 57 Cobalt, etc.). In other examples, the time-dependent feature  1306  can be implemented by a physical unclonable function (PUF) with properties that vary over time. In other examples, the time-dependent feature  1306  can be implemented by any other suitable material and/or device. 
     The example sensor features  1308  are features of the silicon product that detect and/or otherwise determine the environmental conditions of the SDSi system  1300 . For example, the sensor features  1308  can include a temperature sensor (e.g., a thermometer, etc.), a radiation sensor, a humidity sensor, moisture sensor, and/or any other suitable sensors that can detect and/or otherwise determine the environmental conditions of the SDSi system  1300 . Additionally or alternatively, the sensor features  1308  can include features that enable the silicon product  1305  to interface and/or communicate with the machine that silicon product  1305  is installed. In such examples, the sensor features  1308  enable the time calculator  1302  to receive environmental condition information from sensors associated with the machine. 
       FIG.  14    is a block diagram of the example time calculator  1302  of  FIG.  13   . In the illustrated example of  FIG.  13   , the time calculator  1302  receives an example request  1402  and outputs an example time output  1403 . In the illustrated example of  FIG.  14   , the example time calculator  1302  includes an example request interface  1404 , an example property checker  1406 , an example relative time determiner  1408 , and an example absolute time determiner  1410 . While the example time calculator  1302  is depicted as being part of the example silicon product  1305 , some or all of the example time calculator  1302  can be implemented at another location (e.g., at the manufacturer enterprise system  110 , at the customer enterprise system  115 , etc.). 
     The example request interface  1404  receives the example request  1402  and determines if it is a request for an absolute time and/or a relative time. In some examples, the request interface  1404  can receive the request  1402 . The example request  1402  is a request for the absolute time and/or the relative time. For example, the example request  1402  can include a request for a timestamp corresponding to the current absolute time (e.g., the time/date, etc.) and/or a timestamp corresponding to the relative time (e.g., the time between the request and an event in the past, etc.). The example request  1402  can be generated by the analytics engine  206  when logging SDSi feature activation and/or deactivation. In such examples, the analytic engine  206  can query the time calculator  1302  to generate an odometer reading (e.g., a timestamp, a time reference, etc.). In some examples, the license processor  214  can query the time calculator  1302  to determine if a license has expired and, thusly, the feature associated with the license should be deactivated and/or disabled. 
     The example property checker  1406  interfaces with the example time-dependent feature  1306  to determine the current electrical properties of the time-dependent feature  1306 . For example, the property checker  1406  can determine the electrical property of the time-dependent feature  1306  by causing a current to run through time-dependent feature  1306  so the property checker  1406  can check the electrical property of the time-dependent feature  1306 . In other examples, the property checker  1406  can retrieve a log of the electrical properties from a database associated with the silicon product  1305 . In such examples, the property checker  1406  can determine the electrical properties based on recent operations of the silicon product  1305 . 
     The example relative time determiner  1408  correlates the determined property with a relative time. For example, the relative time determiner  1408  can determine the relative time between a current request and a previous event based on (i) the time-dependent function of the time-dependent feature  1306 , and (ii) a previously determined property of the time-dependent feature  1306  corresponding to when the previous event occurred. In some examples, the relative time determiner  1408  can determine the relative time between the request  1401  and a time of manufacturer of the silicon product  1305 . In some examples, the relative time determiner  1408  can determine the relative time between the current event and any previous event in which the electrical property of the time-dependent feature  1306  was determined (e.g., the activation of a feature of the silicon product  1305 , etc.). 
     The example absolute time determiner  1410  determines the absolute time. For example, the absolute time determiner  1410  determines the absolute time based on the relative time determined by the relative time determiner  1408 . For example, after determining the relative time between the request and the time of manufacture, the absolute time determiner  1410  can add the relative time to the absolute time at the time of manufacture. In such examples, the absolute time determiner  1410  can retrieve the absolute time at the time of manufacture from storage. 
     While an example manner of implementing the time calculator  1302  of  FIG.  13    is illustrated in  FIG.  15   , one or more of the elements, processes and/or devices illustrated in  FIG.  4    may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example request interface  204 , the example property checker  1406 , the example relative time determiner  1408 , the example absolute time determiner  1410  and/or, more generally, the example time calculator  1302  of  FIG.  14    may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the example request interface  204 , the example property checker  1406 , the example relative time determiner  1408 , the example absolute time determiner  1410  and/or, more generally, the example time calculator  1302  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), programmable controller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example, request interface  204 , the example property checker  1406 , the example relative time determiner  1408 , the example absolute time determiner  1410  is/are hereby expressly defined to include a non-transitory computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. including the software and/or firmware. Further still, the example time calculator  1302  of  FIG.  13    may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG.  14   , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
       FIG.  15    is a block diagram of the example feature group calculator  1304  of  FIG.  13   . The example feature group calculator  1304  includes an example configuration detector  1502 , an example sensor interface  1504 , an example environmental condition determiner  1506 , an example feature weight determiner  1508 , an example group score calculator  1510 , an feature weight database  1512 , an example threshold comparator  1514 , and configuration controller  1516 . 
     The example configuration detector  1502  detects when the silicon product  1305  is configured and/or about to be configured into a new configuration. For example, the configuration detector  1502  can monitor the features of the processor to determine if a feature has been activated, deactivated, and/or modified. For example, the configuration detector  1502  can detect if a core of the processor has been activated and/or deactivated and/or the frequency of a core has been changed. In some examples, the configuration detector  1502  can detect if a new license has been received by the silicon product  1305 . In such examples, the configuration detector  1502  can detect the received license and determine the feature(s) associated therewith. In some examples, the configuration detector  1502  can receive a request from the license processor  214  to determine if the features associated with a license can be enabled. 
     The sensor interface  1504 , included in or otherwise implemented by the feature group calculator  1304 , receives and distributes data detected and/or collected by the sensor features  1308 . In some examples, the sensor interface  1504  can distribute collected sensor data to the environmental condition determiner  1506 , the group score calculator  1510 , and/or the feature weight database  1512 . In some examples, the sensor interface  1504  can transform the collected sensor data into a format readable by the other components of the feature group calculator  1304 . In some examples, the sensor interface  1504  can receive data associated with the ambient temperature, the humidity, ambient radiation, ambient moisture etc. 
     The environmental condition determiner  1506  determines the environmental conditions and associated environmental weight factors based on the sensor data received by the sensor interface  1504 . For example, the environmental condition determiner  1506  can determine the weight factor associated with each environmental condition. In some examples, the environmental condition determiner  1506  can assign a weight factor to the ambient temperature if the ambient temperature exceeds a boundary condition (e.g., the environmental condition determiner  1506  can determine a weight of 10 if the ambient temperature exceeds 20 degrees Celsius (C), the environmental condition determiner can determine a weight of 30 if the ambient humidity exceeds 80%, etc.). In some examples, the environmental condition determiner  1506  can scale (e.g., linearly, exponentially, etc.) the determined weight as the environmental conditions become worse for processor performance (e.g., the environmental condition determiner  1506  can scale the temperature weight by 5 for each degree above 20 degrees Celsius (C), etc.). In some examples, the environmental condition determiner  1508  can determine a negative weight value if the environmental conditions are favorable (e.g., determining a temperature weight of −5 if the temperature is below 10 degrees Celsius (C), etc.). In some examples, the environmental condition determiner  1506  determines the environmental weight(s) based on previously determined empirical measurements. Additionally or alternatively, the environmental condition determiner  1506  can determine the weight values based on a machine learning model. In some examples, the environmental condition determiner  1506  can determine a total environmental score based on the sum of the determined environmental weights. In some examples, the environmental condition determiner  1506  can determine multiple environmental scores corresponding to each feature group affected by the configuration. In such examples, the determined weights can vary based on the associated feature group. 
     The feature weight determiner  1508  determines the weight of each feature associated with the detected/received configuration. For example, the feature weight determiner  1508  can interface with the feature weight database  1512  to determine the weight value of each feature associated with the detected/received configuration. For example, the feature weight determiner  1508  can determine a score for each feature associated with the configuration. For example, if the configuration includes 8 cores operating at 4.0 gigahertz (GHz), the feature weight determiner  1508  can determine that each operating core has a weight of 10 and that the operating frequency has a weight of 50. In such examples, the feature weight determiner  1508  can determine the feature score of 130. In such examples, the feature score is associated with the operating conditions (e.g., TDP, etc.) of the processor, without regard for the environmental conditions of the processor. 
     The group score calculator  1510  determines the group score based on the environmental score, as determined by the environmental condition determiner  1506 , and the feature score, as determined by the feature weight determiner  1508 . For example, the group score calculator  1510  can determine the group score based on the sum of environmental score and the feature score. In other examples, the group score calculator  1510  can determine the group score based on any other suitable manner (e.g., weighting the feature scores or the environmental score before summing them, multiplying the feature score and the environmental score, etc.). In some examples, the algorithm used by the group score calculator  1510  can be implemented by a machine learning algorithm. In such examples, the machine learning algorithm can be trained by the model trainer  1518 . In some examples, if the configuration affects features from multiple groups, the group score calculator  1510  can determine multiple group scores for each group. 
     The threshold comparator  1514  compares the group score to one or more threshold(s). For example, the threshold comparator  1514  can determine if the group score exceeds a warranty threshold, an enablement threshold, etc. In some examples, the threshold comparator  1514  can have a dynamic threshold (e.g., determined via machine learning, etc.). In some examples, the threshold comparator  1514  can compare the calculated group score depending on the feature group being examined. For example, a first feature-group (e.g., core activation and speed, etc.) and a second feature-group (e.g., memory architecture, speed select, etc.) can have different thresholds associated therewith. 
     The configuration controller  1516  determines an appropriate action to take based on the output of the threshold comparator  1516 . For example, if the group-score exceeds the enablement threshold, the configuration controller  1516  could prevent the configuration from being implemented. In other examples, if the group-score exceeds the warranty threshold but not the enablement threshold, the configuration controller could void the warranty of the processor. In other examples, the configuration controller  1516  can trigger any other suitable action based on the determined group score and/or output of the threshold comparator. 
     While an example manner of implementing the time calculator  1302  of  FIG.  13    is illustrated in  FIG.  15   , one or more of the elements, processes and/or devices illustrated in FIG.  4  may be combined, divided, re-arranged, omitted, eliminated and/or implemented in any other way. Further, the example configuration detector  1502 , sensor interface  1504 , environmental condition determiner, feature weight determiner  1508 , group score calculator  1510 , threshold comparator  1514 , configuration controller  1516 , and/or, more generally, the example feature group calculator  1304  of  FIG.  15    may be implemented by hardware, software, firmware and/or any combination of hardware, software and/or firmware. Thus, for example, any of the configuration detector  1502 , sensor interface  1504 , environmental condition determiner, feature weight determiner  1508 , group score calculator  1510 , threshold comparator  1514 , configuration controller  1516  and/or, more generally, the example feature group calculator  1304  could be implemented by one or more analog or digital circuit(s), logic circuits, programmable processor(s), programmable controller(s), graphics processing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)), application specific integrated circuit(s) (ASIC(s)), programmable logic device(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)). When reading any of the apparatus or system claims of this patent to cover a purely software and/or firmware implementation, at least one of the example configuration detector  1502 , sensor interface  1504 , environmental condition determiner, feature weight determiner  1508 , group score calculator  1510 , threshold comparator  1514 , configuration controller  1516  is/are hereby expressly defined to include a non-transitory computer readable storage device or storage disk such as a memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-ray disk, etc. including the software and/or firmware. Further still, the example feature group calculator  1304  of  FIG.  13    may include one or more elements, processes and/or devices in addition to, or instead of, those illustrated in  FIG.  15   , and/or may include more than one of any or all of the illustrated elements, processes and devices. 
     Flowchart Introduction 
     Flowcharts representative of example hardware logic, machine readable instructions, hardware implemented state machines, and/or any combination thereof for implementing the example systems  100 ,  200 ,  1000 ,  1100 ,  1200 , the example silicon product  105  (e.g., the example semiconductor device  105 ), the example manufacturer enterprise system  110 , the example customer enterprise system  115 , the example cloud platform  120 , the example SDSi asset agent  140 , the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication services  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218 , the example feature libraries  220 - 230 , the example product management service  252 , the example customer management service  254 , the example SDSi feature management service  256 , the example SDSi portal  262 , the example SDSi agent management interface  264 , the example manufacturer trusted agent determiner  1102 , the example SDSi client agent  272 , the example platform inventory management service  274 , the example accounts management service  276 , the example entitlement management service  278 , the example trusted agent determiner  1104 , the example certificate validator  1106 , the example anomaly detector  1108 , the example anomaly detection ML model(s)  1110 , the example feature intent determiner  1112 , the example feature intent ML model(s)  1114 , the example TEE identifier  1202 , the example TEE generator  1204 , the example TEE(s)  1205 , the example TEE library  1206 , and/or the example TEE component(s)  1208  are shown in  FIGS.  16 - 18  and  19 - 25   . In these examples, the machine readable instructions may be one or more executable programs or portion(s) of an executable program for execution by a computer processor, such as the processors  2812 ,  2912 ,  3012 ,  3112 , and/or  3212  shown in the example processor platforms  2800 ,  2900 ,  3000 ,  3100 ,  3200  discussed below in connection with  FIGS.  28 - 30  and  31 - 32   . The one or more programs, or portion(s) thereof, may be embodied in software stored on a non-transitory computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, a Blu-ray Disk™, or a memory associated with the processors  2812 ,  2912 ,  3012 ,  3112 , and/or  3212 , but the entire program or programs and/or parts thereof could alternatively be executed by a device other than the processor  2812 ,  2912 ,  3012 ,  3112 , and/or  3212  and/or embodied in firmware or dedicated hardware. Further, although the example program(s) is(are) described with reference to the flowcharts illustrated in  FIGS.  16 - 18  and  19 - 10   , many other methods of implementing the example systems  100 ,  200 ,  1000 ,  1100 , and/or  1200 , the example silicon product  105  (e.g., the example semiconductor device  105 ), the example manufacturer enterprise system  110 , the example customer enterprise system  115 , the example cloud platform  120 , the example SDSi asset agent  140 , the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication services  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218 , the example feature libraries  220 - 230 , the example product management service  252 , the example customer management service  254 , the example SDSi feature management service  256 , the example SDSi portal  262 , the example SDSi agent management interface  264 , the example manufacturer trusted agent determiner  1102 , the example SDSi client agent  272 , the example platform inventory management service  274 , the example accounts management service  276 , the example entitlement management service  278 , the example trusted agent determiner  1104 , the example certificate validator  1106 , the example anomaly detector  1108 , the example anomaly detection ML model(s)  1110 , the example feature intent determiner  1112 , the example feature intent ML model(s)  1114 , the example TEE identifier  1202 , the example TEE generator  1204 , the example TEE(s)  1205 , the example TEE library  1206 , and/or the example TEE component(s)  1208  may alternatively be used. For example, with reference to the flowcharts illustrated in  FIGS.  16 - 18  and  19 - 25   , the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, combined and/or subdivided into multiple blocks. Additionally or alternatively, any or all of the blocks may be implemented by one or more hardware circuits (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware. 
     A flowchart representative of example hardware logic, machine readable instructions, hardware implemented state machines, and/or any combination thereof for implementing the time calculator  1302  of  FIGS.  13  and  14    is shown in  FIG.  26   . The machine readable instructions may be one or more executable programs or portion(s) of an executable program for execution by a computer processor and/or processor circuitry, such as the processor  3312  shown in the example processor platform  3300  discussed below in connection with  FIG.  33   . The program may be embodied in software stored on a non-transitory computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associated with the processor  3312 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  3312  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowchart illustrated in  FIG.  26   , many other methods of implementing the example time calculator  1302  may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. Additionally or alternatively, any or all of the blocks may be implemented by one or more hardware circuits (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware. The processor circuitry may be distributed in different network locations and/or local to one or more devices (e.g., a multi-core processor in a single machine, multiple processors distributed across a server rack, etc.). 
     A flowchart representative of example hardware logic, machine readable instructions, hardware implemented state machines, and/or any combination thereof for implementing the feature group calculator  1304  of  FIGS.  13  and  15    is shown in  FIG.  27   . The machine readable instructions may be one or more executable programs or portion(s) of an executable program for execution by a computer processor and/or processor circuitry, such as the processor  3312  shown in the example processor platform  3300  discussed below in connection with  FIG.  33   . The program may be embodied in software stored on a non-transitory computer readable storage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, a Blu-ray disk, or a memory associated with the processor  3312 , but the entire program and/or parts thereof could alternatively be executed by a device other than the processor  3312  and/or embodied in firmware or dedicated hardware. Further, although the example program is described with reference to the flowchart illustrated in  FIG.  27   , many other methods of implementing the example feature group calculator  1304  may alternatively be used. For example, the order of execution of the blocks may be changed, and/or some of the blocks described may be changed, eliminated, or combined. Additionally or alternatively, any or all of the blocks may be implemented by one or more hardware circuits (e.g., discrete and/or integrated analog and/or digital circuitry, an FPGA, an ASIC, a comparator, an operational-amplifier (op-amp), a logic circuit, etc.) structured to perform the corresponding operation without executing software or firmware. The processor circuitry may be distributed in different network locations and/or local to one or more devices (e.g., a multi-core processor in a single machine, multiple processors distributed across a server rack, etc.). 
     The machine readable instructions described herein may be stored in one or more of a compressed format, an encrypted format, a fragmented format, a compiled format, an executable format, a packaged format, etc. Machine readable instructions as described herein may be stored as data (e.g., portions of instructions, code, representations of code, etc.) that may be utilized to create, manufacture, and/or produce machine executable instructions. For example, the machine readable instructions may be fragmented and stored on one or more storage devices and/or computing devices (e.g., servers). The machine readable instructions may require one or more of installation, modification, adaptation, updating, combining, supplementing, configuring, decryption, decompression, unpacking, distribution, reassignment, compilation, etc. in order to make them directly readable, interpretable, and/or executable by a computing device and/or other machine. For example, the machine readable instructions may be stored in multiple parts, which are individually compressed, encrypted, and stored on separate computing devices, wherein the parts when decrypted, decompressed, and combined form a set of executable instructions that implement a program such as that described herein. 
     In another example, the machine readable instructions may be stored in a state in which they may be read by a computer, but require addition of a library (e.g., a dynamic link library (DLL)), a software development kit (SDK), an application programming interface (API), etc. in order to execute the instructions on a particular computing device or other device. In another example, the machine readable instructions may need to be configured (e.g., settings stored, data input, network addresses recorded, etc.) before the machine readable instructions and/or the corresponding program(s) can be executed in whole or in part. Thus, the disclosed machine readable instructions and/or corresponding program(s) are intended to encompass such machine readable instructions and/or program(s) regardless of the particular format or state of the machine readable instructions and/or program(s) when stored or otherwise at rest or in transit. 
     The machine readable instructions described herein can be represented by any past, present, or future instruction language, scripting language, programming language, etc. For example, the machine readable instructions may be represented using any of the following languages: C, C++, Java, C#, Perl, Python, JavaScript, HyperText Markup Language (HTML), Structured Query Language (SQL), Swift, etc. 
     As mentioned above, the example processes of  FIGS.  16 - 18 ,  19 - 25  and/or  26    may be implemented using executable instructions (e.g., computer and/or machine readable instructions) stored on a non-transitory computer and/or machine readable medium such as a hard disk drive, a flash memory, a read-only memory, a compact disk, a digital versatile disk, a cache, a random-access memory and/or any other storage device or storage disk in which information is stored for any duration (e.g., for extended time periods, permanently, for brief instances, for temporarily buffering, and/or for caching of the information). As used herein, the term non-transitory computer readable medium is expressly defined to include any type of computer readable storage device and/or storage disk and to exclude propagating signals and to exclude transmission media. Also, as used herein, the terms “computer readable” and “machine readable” are considered equivalent unless indicated otherwise. 
     “Including” and “comprising” (and all forms and tenses thereof) are used herein to be open ended terms. Thus, whenever a claim employs any form of “include” or “comprise” (e.g., comprises, includes, comprising, including, having, etc.) as a preamble or within a claim recitation of any kind, it is to be understood that additional elements, terms, etc. may be present without falling outside the scope of the corresponding claim or recitation. As used herein, when the phrase “at least” is used as the transition term in, for example, a preamble of a claim, it is open-ended in the same manner as the term “comprising” and “including” are open ended. The term “and/or” when used, for example, in a form such as A, B, and/or C refers to any combination or subset of A, B, C such as (1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) B with C, and (7) A with B and with C. As used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. Similarly, as used herein in the context of describing structures, components, items, objects and/or things, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. As used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A and B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. Similarly, as used herein in the context of describing the performance or execution of processes, instructions, actions, activities and/or steps, the phrase “at least one of A or B” is intended to refer to implementations including any of (1) at least one A, (2) at least one B, and (3) at least one A and at least one B. 
     As used herein, singular references (e.g., “a”, “an”, “first”, “second”, etc.) do not exclude a plurality. The term “a” or “an” entity, as used herein, refers to one or more of that entity. The terms “a” (or “an”), “one or more”, and “at least one” can be used interchangeably herein. Furthermore, although individually listed, a plurality of means, elements or method actions may be implemented by, e.g., a single unit or processor. Additionally, although individual features may be included in different examples or claims, these may possibly be combined, and the inclusion in different examples or claims does not imply that a combination of features is not feasible and/or advantageous. 
     Software Defined Silicon Architecture 
     An example program  1600  that may be executed to implement the example manufacturer enterprise system  110  of the example systems  100  and/or  200  of  FIGS.  1 - 2    is illustrated in  FIG.  16   . The example program  1600  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. With reference to the preceding figures and associated written descriptions, the example program  1600  of  FIG.  16    begins execution at block  1605  at which the customer management service  254  of the manufacturer enterprise system  110  on-boards a customer for access to SDSi capabilities offered by the manufacturer of the SDSi product  105 , as described above. For example, the customer management service  254  can exchange information with the customer enterprise system  115  of the customer to on-board the customer prior to or after the customer&#39;s purchase of the SDSi product  105 . 
     At block  1610 , the SDSi portal  262  of the manufacturer enterprise system  110  receives a request to activate (or deactivate) an SDSi feature of the SDSi product  105 , as described above. In the illustrated example, the request is forwarded to the SDSi feature management service  256 , which identifies the SDSi product  105  associated with the request and determines whether the request is valid. Assuming the request is valid, at block  1615 , the SDSi feature management service  256  initiates a query to determine whether the SDSi feature to be activated (or deactivated) is supported by the SDSi product  105 . In some examples, the SDSi feature management service  256  invokes the SDSi agent management interface  264  of the manufacturer enterprise system  110  to send the query directly to the SDSi product  105 , as described above, thereby directly querying the SDSi product  105 . In some examples, the SDSi feature management service  256  sends the query to the SDSi client agent  272  of the client enterprise system  115 , which then sends the query to the SDSi product  105 , as described above, thereby indirectly querying the SDSi product  105 . In some examples, the SDSi agent management interface  264  queries one or more database and/or other data structure(s) maintained by the manufacturer enterprise system  110  to determine whether the SDSi product  105  supports the SDSi feature to be activated (or deactivated), as described above. 
     If the requested SDSi feature is not supported by the SDSi product  105  (block  1620 ), at block  1625  the manufacturer enterprise system  110  performs error handling and denies the request, such as by sending an appropriate communication via the SDSi portal  262  to the customer enterprise system  115 . However, if the requested SDSi feature is supported by the SDSi product  105  (block  1620 ), at block  1630  the SDSi feature management service  256  of the manufacturer enterprise system  110  generates a license to activate (or deactivate) the SDSi feature in response to the customer&#39;s request, as described above. At block  1635 , the SDSi feature management service  256  causes the license to be sent via the SDSi portal  262  to the SDSi client agent  272  of the customer enterprise system  115 , as described above. 
     Sometime later, at block  1640 , the manufacturer enterprise system  110  receives, as described above, a certificate reported by the SDSi product  105  to confirm activation (or deactivation) of the requested SDSi feature, which is processed by the SDSi feature management service  256 . In some examples, the certificate is received directly from the SDSi product  105  by the SDSi agent management interface  264  of the manufacturer enterprise system  110 . In some examples, the certificate is received indirectly, such as from the SDSi client agent  272  of the client enterprise system  115 , which received the certificate form the SDSi product  105 . At block  1640 , the SDSi feature management service  256  of the manufacturer enterprise system  110  processes the received certificate, as described above, to confirm successful activation (or deactivation) of the requested SDSi feature, and invokes the customer management service  254  to reconcile billing, generate an invoice, etc., which contacts the customer enterprise system  115  accordingly. Thereafter, at block  1645 , the SDSi feature management service  256  of the manufacturer enterprise system  110  receives telemetry data reported by the SDSi product  105  (e.g., in one or more certificates), as described above, which is processed at the manufacturer enterprise system  110  to confirm proper operation of the SDSi product  105 , reconcile billing, generate further invoice(s), etc. 
     An example program  1700  that may be executed to implement the example customer enterprise system  115  of the example systems  100  and/or  200  of  FIGS.  1 - 2    is illustrated in  FIG.  17   . The example program  1700  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. With reference to the preceding figures and associated written descriptions, the example program  1700  of  FIG.  17    begins execution at block  1705  at which the accounts management service  276  of the customer enterprise system  115  on-boards its customer for access to SDSi capabilities offered by a manufacturer of the SDSi product  105 , as described above. For example, the accounts management service  276  can exchange information with the manufacturer enterprise system  110  to on-board with the manufacturer prior to or after the customer&#39;s purchase of the SDSi product  105 . 
     At block  1710 , the SDSi client agent  272  of the client enterprise system  115  sends a request to activate (or deactivate) an SDSi feature of the SDSi product  105 , as described above. In the illustrated example, the request is generated by the platform inventory management service  274  or the SDSi client agent  272  of the client enterprise system  115 , and is sent by the SDSi client agent  272  to the SDSi portal  262  of the manufacturer enterprise system  110 . At block  1715 , the SDSi client agent  272  receives a notification from the SDSi portal  262  that indicates whether the requested SDSi feature to be activated (or deactivated) is supported by the SDSi product  105 . If the requested SDSi feature is not supported by the SDSi product  105  (block  1720 ), at block  1725  the customer enterprise system  115  performs error handling and, for example, updates the platform inventory management service  274  to note that the requested SDSi feature is not supported by the SDSi product  105 . However, if the requested SDSi feature is supported by the SDSi product  105  (block  1720 ), at block  1730  the SDSi client agent  272  receives, from the SDSi portal  262 , a license to activate (or deactivate) the SDSi feature in response to the customer&#39;s request, as described above. In the illustrated example, the license is maintained by the entitlement management service  278  of the customer enterprise system  115  until the customer is ready to invoke the license, as described above. 
     Sometime later, at block  1735 , the entitlement management service  278  determines (e.g., based on customer input) that the license received at block  1730  to activate (or deactivate) the SDSi feature is to be invoked. Thus, the entitlement management service  278  provides the license to the SDSi client agent  272 , which sends (e.g., downloads) the license to the SDSi product  105 , as described above. Sometime later, at block  1740 , the customer enterprise system  115  receives, as described above, a certificate reported by the SDSi product  105  to confirm activation (or deactivation) of the requested SDSi feature. In the illustrated example, the certificate is received directly from the SDSi product  105  by the SDSi client agent  272  of the customer enterprise system  115 . At block  1740 , the entitlement management service  278  of the customer enterprise system  115  processes the received certificate, as described above, to confirm successful activation (or deactivation) of the requested SDSi feature, and invokes the accounts management service  276  to reconcile billing, authorize payment, etc., which contacts the manufacturer enterprise system  110  accordingly. Thereafter, at block  1745 , the SDSi client agent  272  of the customer enterprise system  115  receives feature status data reported by the SDSi product  105  (e.g., in one or more certificates), as described above, which is processed at the entitlement management service  278  and accounts management service  276  to confirm proper operation of the SDSi product  105 , reconcile billing, authorize further payment(s), etc. 
     An example program  1800  that may be executed to implement the example SDSi asset agent  140  of the example systems  100  and/or  200  of  FIGS.  1 - 2    is illustrated in  FIG.  18   . The example program  1800  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. With reference to the preceding figures and associated written descriptions, the example program  1800  of  FIG.  18    begins execution at block  1805  at which the SDSi asset agent  140  receives a query to confirm whether a particular SDSi feature is supported by the SDSi product  105 . For example, the query may be from the SDSi agent management interface  264  of the manufacturer enterprise system  110 , the SDSi client agent  272  of the customer enterprise system  115 , etc. At block  1810 , the SDSi asset agent  140  invokes its license processor  214  to generate a response to the query, as describes above. For example, the license processor  214  analyzes the configuration of the hardware circuitry  125 , the firmware  130  and/or the BIOS  135  of the SDSi product  105 , and generates feature support verification information indicating whether the queried feature is supported by the SDSi product  105 , which is reported by the SDSi asset agent  140  back to the source of the query. 
     At block  1815 , the SDSi asset agent  140  receives a license from the SDSi client agent  272  of the customer enterprise system  115  to activate (or deactivate) an SDSi feature of the SDSi product  105 , as described above. At block  1820 , the license processor  214  of the SDSi asset agent  140  verifies the license. For example, the license processor  214  may determine the license is verified when the license correctly identifies the SDSi product  105  and/or the feature to be activated (or deactivated), when the license is authentic (e.g., based on a manufacturer signature included with the license, a license sequence number included in the license, etc.), when activation (or deactivation) of the requested SDSi feature will not result in an unsupported or otherwise invalid configuration of the SDSi product  105 , etc., or any combination thereof. In some examples, the license processor  214  determines the license is verified if some or all such verification criteria are satisfied, and determines the license is unverified if one or more of such verification criteria are not satisfied. 
     If the license is determined to be unverified (block  1825 ), at block  1830  the SDSi asset agent  140  performs error handling to, for example, discard the license and report a certificate to the SDSi client agent  272  that indicates the license could not be invoked. However, if the license is determined to be valid (block  1825 ), at block  1835  the license processor  214  configures the SDSi product  105  to activate (or deactivate) the SDSi feature in accordance with the license, as described above. If configuration is not successful (block  1840 ), at block  1830  the SDSi asset agent  140  performs error handling to, for example, discard the license and report a certificate to the SDSi client agent  272  that indicates the configuration of the SDSi product  105  to activate (or deactivate) the requested SDSi feature was unsuccessful. 
     However, if configuration is successful (block  1840 ), at block  1845  the license processor  214 , in combination with the analytics engine  205 , generates a certificate to confirm the successful activation (or deactivation) of the requested SDSi feature, which is reported by the SDSi asset agent  140  to the SDSi client agent  272 , as described above. Sometime later (e.g., in response to a request, based on an event, etc.), at block  1850 , the SDSi asset agent  140  reports telemetry data and feature status data (e.g., in one or more certificates) to the SDSi client agent  272  of the customer enterprise system  115  and/or to the SDSi agent management interface  264  of the manufacturer enterprise system  110 , as described above. 
     Software Defined Silicon Security 
     An example program  1900  that may be executed to implement the example SDSi asset agent  140  of the example systems  100  and/or  200  of  FIGS.  1 - 2    and/or the example SDSi asset agents  140 A-C of the example systems  1000 ,  1100 , and/or  1200  of  FIGS.  10 - 12    is illustrated in  FIG.  19   . The example program  1900  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. With reference to the preceding figures and associated written descriptions, the example program  1900  of  FIG.  19    begins execution at block  1902  at which the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether a request for a feature activation and/or deactivation has been received. For example, the license processor  214  ( FIG.  2   ) determines that a request to activate one of the features  232 ,  234 ,  236 ,  238 ,  240 ,  242  has been received. 
     At block  1904 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine agent reputation score(s). For example, the trusted agent determiner  1104  ( FIG.  11   ) executes an attestation process of one(s) of the SDSi agents  140 A-C of the mesh network  1002  of  FIG.  10   . An example process that may be executed to implement block  1904  is described below in connection with  FIG.  20   . 
     At block  1906 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C select a trusted agent to transmit the request based on the agent reputation score(s). For example, the trusted agent determiner  1104  selects the first SDSi asset agent  140 A as a sender (e.g., a trusted sender) and/or an issuer (e.g., a trusted issuer) to transmit the request to the manufacturer enterprise system  110  based on the first SDSi asset agent  140 A having the highest agent reputation score of the SDSi asset agents  140 A-C. 
     At block  1908 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C facilitate provisioning of a license to an SDSi agent. For example, the license processor  214  to process a license issued by the manufacturer enterprise system  110  to configure (e.g., activate) an SDSi feature included in the feature sets  232 - 242  implemented by the hardware circuitry  125 , firmware  130 , and/or BIOS  135  of the SDSi semiconductor device  105 . 
     At block  1910 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C perform certificate processing to confirm feature activation and/or deactivation. For example, the certificate validator  1106  ( FIG.  11   ) generates a certificate to confirm the successful activation of the SDSi feature. 
     At block  1912 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C broadcast certificate data to the mesh network  1002 . For example, the certificate validator  1106  of the first SDSi asset agent  140 A broadcasts certificate data including the issued certificate, a current asset status, a value of the activated feature, etc., to the second SDSi asset agent  140 B and the third SDSi asset agent  140 C of the mesh network  1002 . 
     At block  1914 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C update an agent reputation score of the broadcaster. For example, the trusted agent determiner  1104  of the second SDSi asset agent  140 B and the trusted agent determiner  1106  of the third SDSi asset agent  140 C update an agent reputation score of the first SDSi asset agent  140 A included in a list of respective ones of the second SDSi asset agent  140 B and the third SDSi asset agent  140 C. In such examples, the trusted agent determiner  1104  of the second SDSi asset agent  140 B and the trusted agent determiner  1106  of the third SDSi asset agent  140 C update the agent reputation score of the first SDSi asset agent  140 A by increasing the agent reputation score because the successful activation of the license from the manufacturer enterprise system  110  indicates an increased level of trustworthiness of the first SDSi asset agent  140 A. 
     At block  1916 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether to continue monitoring the system. For example, the license processor  214  determines to continue monitoring the system  1000  and/or  1100  for another request to activate and/or deactivate a feature of one of the semiconductor devices  105 A-C has been received. 
     If, at block  1916 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine to continue monitoring the system, control returns to block  1902  to determine whether another request for feature activation and/or deactivation has been received. If, at block  1916 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine not to continue monitoring the system, the example program  1900  of the example of  FIG.  19    concludes. 
     An example program  2000  that may be executed to implement the example SDSi asset agent  140  of the example systems  100  and/or  200  of  FIGS.  1 - 2    and/or the example SDSi asset agents  140 A-C of the example systems  1000 ,  1100 , and/or  1200  of  FIGS.  10 - 12    is illustrated in  FIG.  20   . The example program  2000  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. The example program  2000  of  FIG.  20    may be executed to implement block  1904  of the example of  FIG.  19    to determine agent reputation score(s). With reference to the preceding figures and associated written descriptions, the example program  2000  of  FIG.  20    begins execution at block  2002  at which the SDSi asset agent  140  and/or SDSi asset agent  140 A-C obtains certificate(s). For example, the trusted agent determiner  1104  ( FIG.  11   ) of the first SDSi asset agent  140 A obtain one or more certificates from an SDSi agent, such as the second SDSi asset agent  140 B of  FIG.  10   . 
     At block  2004 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C renew certificate(s) associated with trusted agent(s) of a mesh network. For example, the certificate validator  1106  ( FIG.  11   ) of the first SDSi asset agent  140 A deactivates one or more activated features of the second SDSi asset agent  140 B to cause the second SDSi asset agent  140 B to renew certificate(s) associated with the one or more deactivated features. An example process that may be executed to implement block  2004  is described below in connection with  FIG.  21   . 
     At block  2006 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C obtain renewed certificate(s). For example, the trusted agent determiner  1104  of the first SDSi asset agent  140 A obtains zero, one, or more renewed certificates from the second SDSi asset agent  140 B. 
     At block  2008 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C obtain agent information. For example, the trusted agent determiner  1104  of the first SDSi asset agent  140 A obtains agent information, such as an identifier of the second semiconductor device  105 B, telemetry data reported by the second SDSi asset agent  140 B, etc., from the second SDSi asset agent  140 B. 
     At block  2010 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C execute machine learning model(s) to detect anomalies. For example, the anomaly detector  1108  ( FIG.  11   ) executes the anomaly detection model(s)  1110  to determine whether an anomaly is detected in connection with the certificate(s), the renewed certificate(s), the agent information, etc., associated with the second SDSi asset agent  140 B. 
     At block  2012 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C compiles agent reputation score data. For example, the trusted agent determiner  1104  of the first SDSi asset agent  140 A compiles agent reputation score data including the certificate(s), the renewed certificate(s), the agent information, etc., associated with the second SDSi asset agent  140 B. 
     At block  2014 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine agent reputation score(s) based on the agent reputation score data. For example, the trusted agent determiner  1104  of the first SDSi asset agent  140 A determines an agent reputation score of the second SDSi asset agent  140 B based on the agent reputation score data. In other examples, the trusted agent determiner  1104  of the first SDSi asset agent  140 A is identified as a trusted sender to transmit the agent reputation score data to the manufacturer trusted agent determiner  1102  ( FIG.  11   ). In such examples, the manufacturer trusted agent determiner  1102  determines the agent reputation score of the second SDSi asset agent  140 B based on the agent reputation score data. 
     At block  2016 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C broadcast agent reputation score(s) to the mesh network  1002 . For example, the first SDSi asset agent  140 A broadcasts the agent reputation score of the second SDSi asset agent  140 B to the mesh network  1002 . In some examples, the manufacturer trusted agent determiner  1102   140 A broadcasts the agent reputation score of the second SDSi asset agent  140 B to the mesh network  1002 . 
     At block  2018 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C identify trusted agent(s) based on the agent reputation score(s). For example, the trusted agent determiner  1104  of the first SDSi asset agent  140 A and/or the third SDSi asset agent  140 C identifies the second SDSi asset agent  140 B as a trusted agent in response to the agent reputation score of the second SDSi asset agent  140 B satisfying and/or otherwise meeting a threshold. In response to identifying the trusted agent(s) based on the agent reputation score(s) at block  2018 , the example program  2000  of the example of  FIG.  20    concludes. For example, the program  2000  returns to block  1906  of the example of  FIG.  19    to select a trusted agent to transmit the request based on the agent reputation score(s). 
     An example program  2100  that may be executed to implement the example SDSi asset agent  140  of the example systems  100  and/or  200  of  FIGS.  1 - 2    and/or the example SDSi asset agents  140 A-C of the example systems  1000 ,  1100 , and/or  1200  of  FIGS.  10 - 12    is illustrated in  FIG.  21   . The example program  2100  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. With reference to the preceding figures and associated written descriptions, the example program  2100  of  FIG.  21    begins execution at block  2102  at which the SDSi asset agent  140  and/or SDSi asset agent  140 A-C select an agent of interest to renew certificate(s). For example, the certificate validator  1106  ( FIG.  11   ) of the first SDSi asset agent  140 A select the second SDSi asset agent  140 B to renew certificate(s). 
     At block  2104 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine a current asset status, activated feature(s), and/or license issuer(s). For example, the certificate validator  1106  of the second SDSi asset agent  140 B obtains a current asset status, activated feature(s), and/or license issuer(s) associated with the second semiconductor device  105 B. In such examples, the second SDSi asset agent  140 B transmits the current asset status, the activated feature(s), and/or the license issuer(s) information to the certificate validator  1106  of the first SDSi asset agent  140 A. 
     At block  2106 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C de-activate activated feature(s). For example, the certificate validator  1106  of the first SDSi asset agent  140 A transmits a de-activation command, instruction, signal, etc., to the certificate validator  1106  of the second SDSi asset agent  140 B to de-activate one or more features of the second semiconductor device  105 B. 
     At block  2108 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C transmit a renew request by cryptographically signing the determined data. For example, the certificate validator  1106  of the second SDSi asset agent  140 B cryptographically and/or otherwise electronically signs data including at least one of a current asset status, activated feature(s), and/or license issuer(s) associated with the second semiconductor device  105 B. 
     At block  2110 , the SDSi asset agent  140 , the SDSi asset agent  140 A-C, and/or the manufacturer enterprise system  110  ( FIG.  1   ) determine whether to issue renewed certificate(s). For example, the SDSi feature management service  256  ( FIG.  2   ) determines whether to issue renewed certificate(s) to re-activate the de-activated feature(s) of the second semiconductor device  105 B based on the cryptographically signed data. In such examples, the SDSi feature management service  256  determines whether to issue the renewed certificate(s) based on an agent reputation score of the second SDSi asset agent  140 B, a level of trustworthiness of the second SDSi asset agent  140 B, etc. 
     If, at block  2110 , the SDSi asset agent  140 , the SDSi asset agent  140 A-C, and/or the manufacturer enterprise system  110  determine not to issue renewed certificate(s), then, at block  2112 , the SDSi asset agent  140 , the SDSi asset agent  140 A-C, and/or the manufacturer enterprise system  110  broadcast a non-renewal alert to the mesh network  1002 . For example, the SDSi feature management service  256  invokes the SDSi agent management interface  264  ( FIG.  2   ) to broadcast to an alert, an indication, etc., to the mesh network  1002  that the certificate(s) for the second semiconductor device  105 B have not been renewed. In response to broadcasting the non-renewal alert to the mesh network at block  2112 , control proceeds to block  2120  to determine whether to select another agent of interest to renew certificate(s). 
     If, at block  2110 , the SDSi asset agent  140 , the SDSi asset agent  140 A-C, and/or the manufacturer enterprise system  110  determine to issue renewed certificate(s) control proceeds to block  2114  to facilitate provisioning of license(s) to the agent. For example, the SDSi feature management service  256  invokes the SDSi agent management interface  264  to distribute one or more license(s) that correspond to the certificate(s) in the renew request to the second SDSi asset agent  140 B to cause the second SDSi asset agent  140 B to re-activate the de-activated feature(s). 
     At block  2116 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C perform certificate processing to confirm feature activation and/or de-activation. For example, the license processor  214  of the second SDSi asset agent  140 B re-activates the previously de-activated feature(s). In such examples, the certificate validator  1106  of the second SDSi asset agent  140 B generates a certificate (e.g., a renewal certificate) to confirm the activation (e.g., successful activation, successful re-activation, etc.). 
     At block  2118 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C broadcast renewed certificate(s) to the mesh network  1002 . For example, the certificate validator  1106  of the second SDSi asset agent  140 B invokes the agent interface  202  of the second SDSi asset agent  140 B to broadcast the renewed certificate(s) to the mesh network  1002 . In such examples, the broadcast of the renewed certificate(s) cause the trusted agent determiner  1104  of the first SDSi asset agent  140 A and the third SDSi asset agent  140 C to update an agent reputation score of the second SDSi asset agent  140 B based on the renewed certificate(s). 
     At block  2120 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether to select another agent of interest to renew certificate(s). For example, the certificate validator  1106  determines to select the third SDSi asset agent  140 C to renew certificate(s). If, at block  2120 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine to select another agent of interest to renew certificate(s), control returns to block  2102  to select another agent of interest to renew certificate(s). If, at block  2120 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine not to select another agent of interest to renew certificate(s), the example program  2100  concludes. For example, the program  2100  returns to block  2006  of the example of  FIG.  20    to obtain renewed certificate(s). 
     An example program  2200  that may be executed to implement the example SDSi asset agent  140  of the example systems  100  and/or  200  of  FIGS.  1 - 2    and/or the example SDSi asset agents  140 A-C of the example systems  1000 ,  1100 , and/or  1200  of  FIGS.  10 - 12    is illustrated in  FIG.  22   . The example program  2200  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. With reference to the preceding figures and associated written descriptions, the example program  2200  of  FIG.  22    begins execution at block  2202  at which the SDSi asset agent  140  and/or SDSi asset agent  140 A-C select an agent of interest in a mesh network to validate. For example, the trusted agent determiner  1104  ( FIG.  11   ) of the first SDSi asset agent  140 A selects the second SDSi asset agent  140 B of the mesh network  1002  to validate. 
     At block  2204 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C obtain a runtime measurement. For example, the trusted agent determiner  1104  of the second SDSi asset agent  140 B generates a runtime measurement (e.g., a hash of application code, a value of a CPU counter, etc.). In such examples, the trusted agent determiner  1104  of the second SDSi asset agent  140 B signs the runtime measurement and transmits the signed runtime measurement to the trusted agent determiner  1104  of the first SDSi asset agent  140 A. 
     At block  2206 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C compare the runtime measurement against a known validated measurement. For example, the trusted agent determiner  1104  of the first SDSi asset agent  140 A compares the signed runtime measurement to a known validated measurement stored in the first SDSi asset agent  140 A, the manufacturer enterprise system  110 , and/or the customer enterprise system  115 . 
     At block  2208 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C broadcast the validation result to the mesh network. For example, the trusted agent determiner  1104  of the first SDSi asset agent  140 A transmits the result of the comparison (e.g., the comparison yielded a match, a mismatch, etc.) to the second SDSi asset agent  140 B, the third SDSi asset agent  140 C, etc., of the mesh network  1002 . 
     At block  2210 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether the validation result indicates a comparison match. For example, the third SDSi asset agent  140 C obtains the validation result and determines that the comparison of the runtime measurement to the known validated measurement is a match, a mismatch, etc. If, at block  2210 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine the validation result indicates a comparison match, then, at block  2212 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C store the validation result and increase an agent reputation score. For example, the trusted agent determiner  1104  of the third SDSi asset agent  140 C increases an agent reputation score of the second SDSi asset agent  140 B because the comparison match indicates an increased level of trustworthiness of the second SDSi asset agent  140 B. In such examples, the trusted agent determiner  1104  of the third SDSi asset agent  140 C stores the validation result to use in a subsequent or future attestation process in connection with runtime measurements. In response to storing the validation result and increasing the agent reputation score at block  2214 , control proceeds to block  2218  to determine whether to select another agent of interest to validate. 
     If, at block  2210 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine the validation result does not indicate a comparison match, control proceeds to block  2214  to store the validation result and decrease an agent reputation score. For example, the trusted agent determiner  1104  of the third SDSi asset agent  140 C decreases an agent reputation score of the second SDSi asset agent  140 B because the comparison mismatch indicates a decreased level of trustworthiness of the second SDSi asset agent  140 B. 
     At block  2216 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C transmit a failure report to enterprise system(s). For example, the trusted agent determiner  1104  of the first SDSi asset agent  140 A and/or the third SDSi asset agent  140 C transmit(s) a failure report including an instance receipt detailing the runtime measurement, the known validated measurement, the result of the comparison, a timestamp, an identifier of the SDSi agent executing the comparison, an identifier of the SDSi agent generating the report, etc., to the manufacturer enterprise system  110  and/or the customer enterprise system  115 . 
     At block  2218 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether to select another agent of interest to validate. For example, the first SDSi asset agent  140 A determines to select the third SDSi asset agent  140 C to validate. If, at block  2218 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine to select another agent of interest to validate, control returns to block  2202  to select another agent of interest in the mesh network  1002  to validate. If, at block  2218 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine not to select another agent of interest to validate, the example program  2200  concludes. 
     An example program  2300  that may be executed to implement the example SDSi asset agent  140  of the example systems  100  and/or  200  of  FIGS.  1 - 2    and/or the example SDSi asset agents  140 A-C of the example systems  1000 ,  1100 , and/or  1200  of  FIGS.  10 - 12    is illustrated in  FIG.  23   . The example program  2300  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. With reference to the preceding figures and associated written descriptions, the example program  2300  of  FIG.  23    begins execution at block  2302  at which the SDSi asset agent  140  and/or SDSi asset agent  140 A-C distributes a trusted execution environment (TEE) handler to an agent. For example, the manufacturer enterprise network  110  and/or the customer enterprise network  115  distribute the TEE generator  1204  to one(s) of the SDSi asset agents  140 A-C, such as the first SDSi asset agent  140 A. In such examples, the TEE generator  1204  implements a TEE handler that generates a TEE, deploys the TEE, and/or returns an abstracted instance of the TEE to which an element of the first SDSi asset agent  140 A or external computing system can interact and/or otherwise control via one or more hardware-agnostic TEE APIs. 
     At block  2304 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C explore an environment for known TEE(s) based on security requirements. For example, the TEE identifier  1202  explores, searches, queries, etc., at least one of the TEE(s)  1205 , the TEE library  1206 , or the TEE component(s)  1208  for a known, pre-packaged, and/or pre-configured TEE that meets and/or otherwise satisfies the security requirements. 
     At block  2306 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether a known TEE has been identified. For example, the TEE identifier  1202  identifies a known TEE of the TEE(s)  1205  that satisfies the security requirements. In other examples, the TEE identifier  1202  does not identify a known TEE of the TEE(s)  1205  that satisfies the security requirements. 
     If, at block  2306 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that a known TEE has not been identified, control proceeds to block  2312  to generate a TEE based on TEE component(s). If, at block  2306 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that a known TEE has been identified, then, at block  2308 , the TEE identifier  1202  identifies the known TEE to deploy. For example, the TEE identifier  1202  invokes the TEE generator  1204  to return one or more TEE APIs from the TEE library  1206  to interface with the identified known TEE. 
     At block  2310 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C deploy application secrets to a deployed TEE. For example, the TEE(s)  1205  obtains application code to execute in the TEE(s)  1205 , cryptographically protected data to store in trusted memory and/or storage of the TEE(s)  1205 , etc. In response to deploying the application secrets to the deployed TEE, the example program  2300  concludes. 
     At block  2312 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C generates a TEE based on TEE component(s). For example, the TEE generator  1204  compiles a TEE from one(s) of the TEE component(s)  1208 , or from TEE component(s) on a remote computing system (e.g., the customer enterprise system  115 , a different one of the SDSi asset agents  140 A-C, etc.). In such examples, the TEE generator  1204  deploys the compiled TEE as one of the TEE(s)  1205 , or as a TEE on the remote computing system. An example process that may be executed to implement block  2312  is described below in connection with  FIG.  24   . 
     At block  2314 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether a TEE has been generated. For example, the TEE generator  1204  determines that a TEE has not been generated because the TEE component(s)  1208  cannot compose a TEE that satisfies the security requirements. In other examples, the TEE generator  1204  determines that a TEE has been generated based on the TEE being deployed to protect data of interest. 
     If, at block  2314 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that a TEE has been generated, the example program  2300  concludes. If, at block  2314 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that a TEE has not been generated, then, at block  2316 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C generate an alert. For example, the TEE generator  1204  generates an alert indicative of a TEE not being generated because necessary one(s) of the TEE component(s)  1208  are not activated, present, etc., the security requirements are too stringent, etc. In response to generating the alert at block  2316 , the example program  2300  concludes. 
     An example program  2400  that may be executed to implement the example SDSi asset agent  140  of the example systems  100  and/or  200  of  FIGS.  1 - 2    and/or the example SDSi asset agents  140 A-C of the example systems  1000 ,  1100 , and/or  1200  of  FIGS.  10 - 12    is illustrated in  FIG.  24   . The example program  2400  may be executed to implement block  2312  of the example of  FIG.  23    to generate a TEE based on TEE component(s). The example program  2400  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. With reference to the preceding figures and associated written descriptions, the example program  2400  of  FIG.  24    begins execution at block  2402  at which the SDSi asset agent  140  and/or SDSi asset agent  140 A-C explore an environment to identify TEE component(s). For example, the TEE identifier  1202  ( FIG.  12   ) explores, searches, queries, etc., at least one of the TEE(s)  1205 , the TEE library  1206 , or the TEE component(s)  1208  for a for hardware, software, and/or firmware TEE related component(s) that meet and/or otherwise satisfy requested security requirements. In such examples, the TEE identifier  302  identifies trusted execution, trusted memory, and trusted storage included in the TEE component(s)  1208  that satisfy the requested security requirements. 
     At block  2404 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether a hardware-based TEE is composable based on the identified TEE component(s). For example, the TEE identifier  1202  determines that the security requirements include trusted execution, trusted memory, and trusted storage. In such examples, the TEE identifier  1202  determines that the TEE component(s)  1208  include the trusted execution, trusted memory, and trusted storage. In some such examples, the TEE identifier  1202  determines that a hardware-based TEE can be composed, generated, etc., based on the trusted execution, trusted memory, and trusted storage. 
     If, at block  2404 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that a hardware-based TEE is composable based on the identified TEE component(s), then, at block  2406 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C deploy a hardware-based TEE to protect application secrets. For example, the TEE generator  1204  deploys a hardware-based TEE as one of the TEE(s)  2405  based on the TEE component(s)  1208 . In response to deploying the hardware-based TEE to protect the application secrets at block  2406 , the example program  2400  concludes. For example, the program  2400  returns to block  2314  of the example of  FIG.  23    to determine whether a TEE has been generated. 
     If, at block  2404 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that a hardware-based TEE is not composable based on the identified TEE component(s), control proceeds to block  2408  to determine whether a software-based TEE is composable based on the security requirements. For example, the TEE identifier  1202  determines that a software-based TEE is composable based on the security requirements. 
     If, at block  2408 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that a software-based TEE is not composable based on the security requirements, the example program  2400  concludes. For example, the program  2400  returns to block  2314  of the example of  FIG.  23    to determine whether a TEE has been generated. 
     If, at block  2408 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that a software-based TEE is composable based on the security requirements, then, at block  2410 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C deploy a software-based TEE to protect application secrets. For example, the TEE generator  1204  deploys a software-based TEE as one of the TEE(s)  2405  based on the TEE library  1206 , the TEE component(s)  1208 , etc., and/or a combination thereof. In response to deploying the software-based TEE to protect the application secrets at block  2410 , the example program  2400  concludes. For example, the program  2400  returns to block  2314  of the example of  FIG.  23    to determine whether a TEE has been generated. 
     An example program  2500  that may be executed to implement the example SDSi asset agent  140  of the example systems  100  and/or  200  of  FIGS.  1 - 2    and/or the example SDSi asset agents  140 A-C of the example systems  1000 ,  1100 , and/or  1200  of  FIGS.  10 - 12    is illustrated in  FIG.  25   . The example program  2500  may be executed at predetermined intervals, based on an occurrence of a predetermined event, etc., or any combination thereof. With reference to the preceding figures and associated written descriptions, the example program  2500  of  FIG.  25    begins execution at block  2502  at which the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether an agent is capable of translating intent into feature(s) to be activated. For example, the first SDSi asset agent  140 A determines that the SDSi asset agent  140 A is capable of translating an intent or intended outcome from a request for a configuration change of the SDSi asset agent  140 A, and/or, more generally, the system  1000 ,  1100 , and/or  1200  of  FIGS.  10 - 12    based on whether the SDSi asset agent  140 A includes and/or otherwise has activated the feature intent determiner  1112  and/or the feature intent ML model(s)  1114 . 
     If, at block  2502 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that the agent is capable of translating intent into feature(s) to be activated, control proceeds to block  2506  to define a high-level meta-language. If, at block  2502 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine that the agent is not capable of translating intent into feature(s) to be activated, then, at block  2504 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C activates an intent translator feature. An example process that may be executed to implement block  2504  is described in connection with  FIGS.  16 ,  17   , and/or  18 . For example, the first SDSi asset agent  140 A requests the manufacturer enterprise system  110  for an license to activate the feature intent determiner  1112  and/or the feature intent ML model(s)  1114 . 
     At block  2506 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C defines a high-level meta-language. For example, the feature intent determiner  1112  defines a high-level meta-language to process configuration change requests. 
     At block  2508 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C obtains a request for a configuration change. For example, the first SDSi asset agent  140 A obtains a request to change the first SDSi asset agent  140 A and/or, more generally, the first semiconductor device  105 A, by adjusting requirement(s) associated with at least one of availability, machine learning, performance, reliability, or security. 
     At block  2510 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C executes machine learning model(s) to translate an intent of the request to feature(s) to activate. For example, the feature intent determiner  1112  invokes the feature intent ML model(s)  1114  to translate a change in performance requirements to an intent or intended outcome of improving performance of the first SDSi asset agent  140 A and/or, more generally, the first semiconductor device  105 A. In such examples, the feature intent ML model(s)  1114  translate the intent or intended outcome to one or more features of the first semiconductor device  105 A to improve performance, such as activating one or more cores of a CPU. 
     At block  2512 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine whether to adjust feature(s) identified by the machine learning model(s). For example, a user, an external computing system, etc., determines to adjust and/or otherwise override the feature(s) identified by the feature intent ML model(s)  1114 . 
     If, at block  2512 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine not to adjust feature(s) identified by the machine learning model(s), control proceeds to block  2516  to activate the feature(s) based on the intent. If, at block  2512 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C determine to adjust feature(s) identified by the machine learning model(s), then, at block  2514 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C re-trains the machine learning model(s) based on the adjustment(s). For example, the feature intent determiner  1112  provides feedback, new data (e.g., new training data), etc., representative of the adjustment(s) to the feature intent ML model(s)  1114  to retrain and deploy retrained one(s) of the feature intent ML model(s)  1114 . 
     At block  2516 , the SDSi asset agent  140  and/or SDSi asset agent  140 A-C activates feature(s) based on the intent. For example, the feature intent determiner  1112  invokes the license processor  214  to facilitate activation of the identified feature(s). In response to activating the feature(s) based on the intent at block  2516 , the example program  2500  of the example of  FIG.  25    concludes. 
     Device Enhancements 
       FIG.  26    is a flowchart representative of example computer readable instructions that may be executed to implement the example time calculator of  FIG.  14   . The process  2600  of  FIG.  26    begins and block  2602 . At block  2602 , the property checker  1406  determines properties of the time-dependent feature  1306  at the time of manufacture. For example, the property checker  1406  can cause a current to run through the time-dependent feature  1306  and record the electrical properties of the time-dependent feature  1306  at the time of manufacture of the silicon product  1305 . 
     At block  2604 , the absolute time determiner  1410  correlates the determined property with the time of manufacture. For example, the absolute time determiner  1410  can store the determined property with the time of manufacture in a storage associated with the silicon product  1305 . The absolute time determiner  1410  can then determine the time of manufacture by communicating with a clock associated with silicon product  1305  and/or by a manual/automatic input by the manufacturer. In such examples, the absolute time determiner  1410  can record the absolute time of the time of manufacture with the determined electrical properties of the time-dependent feature  1306 . 
     At block  2606 , the request interface  1404  receives the request  1402  for absolute time and/or relative time. For example, the request interface  1404  can receive the request  1402  from the analytics engine  206 , license processor  214 , etc. In some examples, the request interface  1404  can determine what time is requested by the request  1402  (e.g., the absolute time, the relative time, or both.). 
     At block  2608 , the property checker  1406  determines the electrical properties of time-dependent feature  1306  at the time of the request  1402 . For example, the property checker  1406  can cause a current to run through the time-dependent feature  1306  such that the electrical properties of the device can be determined. In some examples, the property checker  1406  can determine the current electrical properties by any other suitable means (e.g., reading a log of operations of the silicon product  1305 , etc.). 
     At block  2610 , the relative time determiner  1408  determines the relative time based on a comparison of the properties of the time-dependent feature  1306  at the time of manufacturer properties and the properties of the time-dependent feature  1306  at the current time (e.g., the time of the request  1402 ). For example, based on the known time-variance of the electrical properties of the time-dependent feature  1306 , the relative time determiner  1408  can determine the relative time between the time of manufacture and the current time. In some examples, the relative time determiner  1408  can determine the relative time based on any other suitable means. 
     At block  2612 , the absolute time determiner  1410  determines the absolute time based on a comparison of relative time and time of manufacture. For example, the absolute time determiner  1410  can add the relative time to the stored time of manufacture as recorded during the execution of block  2604 . In some examples, the absolute time determiner  1410  can determine the absolute time by any other suitable means. After determining the absolute time, the absolute time determiner  1410  transmits the determined absolute and/or relative time to the requesting party/entity. 
     At block  2614 , the request interface  1404  determines if another request  1402  has been received. If another request has been received, the process  2600  returns to block  2606 . If another request has not been received, the process  2600  ends. 
       FIG.  27    is a flowchart representative of example computer-readable instructions that may be executed to implement the example feature group calculator  1304  of  FIG.  15   . The process  2700  of  FIG.  27    begins at block  2702 . At block  2702 , the configuration detector  1502  detects a new configuration of silicon product  1305 . For example, the configuration detector  1502  could detect features of the silicon product  1305  have been and/or are to be activated, deactivated, modified, etc. In some examples, the configuration detector  1502  can receive a notification (e.g., sent from the license processor  214 , etc.) indicating a new configuration is/will be activated for the silicon product  1305 . In some examples, the configuration detector  1502  can determine which feature-groups are affected by the configuration. 
     At block  2704 , the feature weight determiner  1508  determines the weight value(s) for each feature of the detected configuration. For example, the feature weight determiner  1508  can determine the weight of each feature associated with the detected configuration. For example, the feature weight determiner  1508  can determine a score for each feature associated with the configuration. For example, if the configuration includes 8 cores operating at 4.0 gigahertz (GHz), the feature weight determiner  1508  can determine that each operating core has a weight of 10 and that the operating frequency has a weight of 50. In such examples, the feature weight determiner  1508  can determine the feature score of 130. 
     At block  2706 , the environmental condition determiner  1506  determines the weight value(s) associated with environmental conditions. For example, the environmental condition determiner  1506 , via the sensor interface  1504 , can determine the environmental conditions under which the silicon product  1305  is operating. For example, the environmental condition determiner  1506  can determine the weight factor associated with each environmental condition. In some examples, the environmental condition determiner  1506  can assign a weight factor to the ambient temperature if the ambient temperature exceeds a boundary condition (e.g., the environmental condition determiner  1506  can determine a weight of 10 if the ambient temperature exceeds 20 degrees Celsius (C), the environmental condition determiner can determine a weight of 30 if the ambient humidity exceeds 80%, etc.). In some examples, the environmental condition determiner  1506  can scale (e.g., linearly, exponentially, etc.) the determined weight as the environmental conditions become worse for processor performance (e.g., the environmental condition determiner  1506  can scale the temperature weight by 5 for each degree above 20 degrees Celsius (C), etc.). 
     At block  2708 , the group score calculator  1510  calculate feature-group score(s). For example, the group score calculator  1510  can determine the group score for each feature group detected by the new configuration detector during the execution of block  2702 . In some examples, the group score calculator  1510  can determine the group score by summing the feature weight score and the environmental weight score. In some examples, the group score calculator  1510  can determine the group score(s) by any other suitable means. 
     At block  2710 , the threshold comparator  1514  determines if at least one feature-group score(s) exceeds a corresponding enablement and/or warranty threshold. For example, the threshold comparator  1514  can compare the calculated group score(s) to at least one threshold and determine if the group score exceeds the at least one threshold. If at least one group score exceeds a threshold, the process  2700  advances to block  2712 . If no group score exceeds a threshold, the process  2700  advances to block  2714 . 
     At block  2712 , the configuration controller  1516  takes action based on an exceeded group combination score. For example, if the configuration controller  1516  can void the warranty of the silicon product  1305  if a group score exceeds the warranty threshold. In some examples, the configuration controller  1516  can prevent the configuration from being enabled. At block  2714 , the configuration controller  1516  enables configuration without additional actions. The process  2700  ends. 
     Processor and Distribution Platforms 
       FIG.  28    is a block diagram of an example processor platform  2800  structured to execute the instructions of  FIGS.  16  and/or  19 - 25    to implement the manufacture enterprise system  110  of  FIGS.  1 - 9  and/or  10 - 12   . The processor platform  2800  can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), or any other type of computing device. 
     The processor platform  2800  of the illustrated example includes a processor  2812 . The processor  2812  of the illustrated example is hardware. For example, the processor  2812  can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer. The hardware processor  2812  may be a semiconductor based (e.g., silicon based) device. In this example, the processor  2812  implements one or more of the example product management service  252 , the example customer management service  254 , the example SDSi feature management service  256 , the example SDSi portal  262 , the example SDSi agent management interface  264 , and/or the manufacturer trusted agent determiner  1102 . 
     The processor  2812  of the illustrated example includes a local memory  2813  (e.g., a cache). The processor  2812  of the illustrated example is in communication with a main memory including a volatile memory  2814  and a non-volatile memory  2816  via a link  2818 . The link  2818  may be implemented by a bus, one or more point-to-point connections, etc., or a combination thereof. The volatile memory  2814  may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAIVIBUS® Dynamic Random Access Memory (RDRAM®) and/or any other type of random access memory device. The non-volatile memory  2816  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  2814 ,  2816  is controlled by a memory controller. 
     The processor platform  2800  of the illustrated example also includes an interface circuit  2820 . The interface circuit  2820  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), a Bluetooth® interface, a near field communication (NFC) interface, and/or a PCI express interface. 
     In the illustrated example, one or more input devices  2822  are connected to the interface circuit  2820 . The input device(s)  2822  permit(s) a user to enter data and/or commands into the processor  2812 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, a trackbar (such as an isopoint), a voice recognition system and/or any other human-machine interface. Also, many systems, such as the processor platform  2800 , can allow the user to control the computer system and provide data to the computer using physical gestures, such as, but not limited to, hand or body movements, facial expressions, and face recognition. 
     One or more output devices  2824  are also connected to the interface circuit  2820  of the illustrated example. The output devices  2824  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube display (CRT), an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer and/or speakers(s). The interface circuit  2820  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip and/or a graphics driver processor. 
     The interface circuit  2820  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  2826 . The communication can be via, for example, an Ethernet connection, a digital subscriber line (DSL) connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, etc. 
     The processor platform  2800  of the illustrated example also includes one or more mass storage devices  2828  for storing software and/or data. Examples of such mass storage devices  2828  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, redundant array of independent disks (RAID) systems, and digital versatile disk (DVD) drives. 
     The machine executable instructions  2832  corresponding to the instructions of  FIG.  16    and/or  FIGS.  19 - 25    may be stored in the mass storage device  2828 , in the volatile memory  2814 , in the non-volatile memory  2816 , in the local memory  2813  and/or on a removable non-transitory computer readable storage medium, such as a CD or DVD  2836 . 
       FIG.  29    is a block diagram of an example processor platform  2900  structured to execute the instructions of  FIG.  17    to implement the customer enterprise system  115  of  FIGS.  1 - 9   . The processor platform  2900  can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™) or any other type of computing device. 
     The processor platform  2900  of the illustrated example includes a processor  2912 . The processor  2912  of the illustrated example is hardware. For example, the processor  2912  can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer. The hardware processor  2912  may be a semiconductor based (e.g., silicon based) device. In this example, the processor  2912  implements one or more of the example SDSi client agent  272 , the example platform inventory management service  274 , the example accounts management service  276  and/or the example entitlement management service  278 . 
     The processor  2912  of the illustrated example includes a local memory  2913  (e.g., a cache). The processor  2912  of the illustrated example is in communication with a main memory including a volatile memory  2914  and a non-volatile memory  2916  via a link  2918 . The link  2918  may be implemented by a bus, one or more point-to-point connections, etc., or a combination thereof. The volatile memory  2914  may be implemented by SDRAM, DRAM, RDRAM® and/or any other type of random access memory device. The non-volatile memory  2916  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  2914 ,  2916  is controlled by a memory controller. 
     The processor platform  2900  of the illustrated example also includes an interface circuit  2920 . The interface circuit  2920  may be implemented by any type of interface standard, such as an Ethernet interface, a USB, a Bluetooth® interface, an NFC interface, and/or a PCI express interface. 
     In the illustrated example, one or more input devices  2922  are connected to the interface circuit  2920 . The input device(s)  2922  permit(s) a user to enter data and/or commands into the processor  2912 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, a trackbar (such as an isopoint), a voice recognition system and/or any other human-machine interface. Also, many systems, such as the processor platform  2900 , can allow the user to control the computer system and provide data to the computer using physical gestures, such as, but not limited to, hand or body movements, facial expressions, and face recognition. 
     One or more output devices  2924  are also connected to the interface circuit  2920  of the illustrated example. The output devices  2924  can be implemented, for example, by display devices (e.g., an LED, an OLED, an LCD, a CRT display, an IPS display, a touchscreen, etc.), a tactile output device, a printer and/or speakers(s). The interface circuit  2920  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip and/or a graphics driver processor. 
     The interface circuit  2920  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  2926 . The communication can be via, for example, an Ethernet connection, a DSL connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, etc. 
     The processor platform  2900  of the illustrated example also includes one or more mass storage devices  2928  for storing software and/or data. Examples of such mass storage devices  2928  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and DVD drives. 
     The machine executable instructions  2932  corresponding to the instructions of  FIG.  17    may be stored in the mass storage device  2928 , in the volatile memory  2914 , in the non-volatile memory  2916 , in the local memory  2913  and/or on a removable non-transitory computer readable storage medium, such as a CD or DVD  2936 . 
       FIG.  30    is a block diagram of an example processor platform  3000  structured to execute the instructions of  FIG.  28    to implement the SDSi asset agent  140  of  FIGS.  1 - 9   . The processor platform  3000  can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, a DVD player, a CD player, a digital video recorder, a Blu-ray player, a gaming console, a personal video recorder, a set top box a digital camera, a headset or other wearable device, or any other type of computing device. 
     The processor platform  3000  of the illustrated example includes a processor  3012 . The processor  3012  of the illustrated example is hardware. For example, the processor  3012  can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer. The hardware processor  3012  may be a semiconductor based (e.g., silicon based) device. In this example, the processor  3012  implements one or more of the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication services  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218  and/or the example feature libraries  220 - 230 . 
     The processor  3012  of the illustrated example includes a local memory  3013  (e.g., a cache). The processor  3012  of the illustrated example is in communication with a main memory including a volatile memory  3014  and a non-volatile memory  3016  via a link  3018 . The link  3018  may be implemented by a bus, one or more point-to-point connections, etc., or a combination thereof. The volatile memory  3014  may be implemented by SDRAM, DRAM, RDRAM® and/or any other type of random access memory device. The non-volatile memory  3016  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  3014 ,  3016  is controlled by a memory controller. 
     The processor platform  3000  of the illustrated example also includes an interface circuit  3020 . The interface circuit  3020  may be implemented by any type of interface standard, such as an Ethernet interface, a USB, a Bluetooth® interface, an NFC interface, and/or a PCI express interface. 
     In the illustrated example, one or more input devices  3022  are connected to the interface circuit  3020 . The input device(s)  3022  permit(s) a user to enter data and/or commands into the processor  3012 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, a trackbar (such as an isopoint), a voice recognition system and/or any other human-machine interface. Also, many systems, such as the processor platform  3000 , can allow the user to control the computer system and provide data to the computer using physical gestures, such as, but not limited to, hand or body movements, facial expressions, and face recognition. 
     One or more output devices  3024  are also connected to the interface circuit  3020  of the illustrated example. The output devices  3024  can be implemented, for example, by display devices (e.g., an LED, an OLED, an LCD, a CRT display, an IPS display, a touchscreen, etc.), a tactile output device, a printer and/or speakers(s). The interface circuit  3020  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip and/or a graphics driver processor. 
     The interface circuit  3020  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  3026 . The communication can be via, for example, an Ethernet connection, a DSL connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, etc. 
     The processor platform  3000  of the illustrated example also includes one or more mass storage devices  3028  for storing software and/or data. Examples of such mass storage devices  3028  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and DVD drives. 
     The machine executable instructions  3032  corresponding to the instructions of  FIG.  18    may be stored in the mass storage device  3028 , in the volatile memory  3014 , in the non-volatile memory  3016 , in the local memory  3013  and/or on a removable non-transitory computer readable storage medium, such as a CD or DVD  3036 . 
       FIG.  31    is a block diagram of an example processor platform  3100  structured to execute the instructions of  FIGS.  19 - 22  and/or  25    to implement the SDSi asset agent  140  of  FIGS.  1 - 9    and/or the SDSi asset agent  140 A-C of  FIGS.  10 - 12   . The processor platform  3100  can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, a DVD player, a CD player, a digital video recorder, a Blu-ray player, a gaming console, a personal video recorder, a set top box a digital camera, a headset or other wearable device, or any other type of computing device. 
     The processor platform  3100  of the illustrated example includes a processor  3112 . The processor  3112  of the illustrated example is hardware. For example, the processor  3112  can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer. The hardware processor  3112  may be a semiconductor based (e.g., silicon based) device. In this example, the processor  3112  implements one or more of the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication services  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218 , the example feature libraries  220 - 230 , the example trusted agent determiner  1104 , the example certificate validator  1106 , the example anomaly detector  1108 , the example anomaly detection ML model(s)  1110 , the example feature intent determiner  1112 , and/or the example feature intent ML model(s)  1114 . 
     The processor  3112  of the illustrated example includes a local memory  3113  (e.g., a cache). The processor  3112  of the illustrated example is in communication with a main memory including a volatile memory  3114  and a non-volatile memory  3116  via a link  3118 . The link  3118  may be implemented by a bus, one or more point-to-point connections, etc., or a combination thereof. The volatile memory  3114  may be implemented by SDRAM, DRAM, RDRAM® and/or any other type of random access memory device. The non-volatile memory  3116  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  3114 ,  3116  is controlled by a memory controller. 
     The processor platform  3100  of the illustrated example also includes an interface circuit  3120 . The interface circuit  3120  may be implemented by any type of interface standard, such as an Ethernet interface, a USB, a Bluetooth® interface, an NFC interface, and/or a PCI express interface. 
     In the illustrated example, one or more input devices  3122  are connected to the interface circuit  3120 . The input device(s)  3122  permit(s) a user to enter data and/or commands into the processor  3112 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, a trackbar (such as an isopoint), a voice recognition system and/or any other human-machine interface. Also, many systems, such as the processor platform  3100 , can allow the user to control the computer system and provide data to the computer using physical gestures, such as, but not limited to, hand or body movements, facial expressions, and face recognition. 
     One or more output devices  3124  are also connected to the interface circuit  3120  of the illustrated example. The output devices  3124  can be implemented, for example, by display devices (e.g., an LED, an OLED, an LCD, a CRT display, an IPS display, a touchscreen, etc.), a tactile output device, a printer and/or speakers(s). The interface circuit  3120  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip and/or a graphics driver processor. 
     The interface circuit  3120  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  3126 . The communication can be via, for example, an Ethernet connection, a DSL connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, etc. 
     The processor platform  3100  of the illustrated example also includes one or more mass storage devices  3128  for storing software and/or data. Examples of such mass storage devices  3128  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and DVD drives. 
     The machine executable instructions  3132  corresponding to the instructions of  FIG.  19 - 22  and/or  25    may be stored in the mass storage device  3128 , in the volatile memory  3114 , in the non-volatile memory  3116 , in the local memory  3113  and/or on a removable non-transitory computer readable storage medium, such as a CD or DVD  3136 . 
       FIG.  32    is a block diagram of an example processor platform  3200  structured to execute the instructions of  FIGS.  23 ,  24   , and/or  25  to implement the SDSi asset agent  140  of  FIGS.  1 - 9    and/or the SDSi asset agent  140 A-C of  FIGS.  10 - 12   . The processor platform  3200  can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, a DVD player, a CD player, a digital video recorder, a Blu-ray player, a gaming console, a personal video recorder, a set top box a digital camera, a headset or other wearable device, or any other type of computing device. 
     The processor platform  3200  of the illustrated example includes a processor  3212 . The processor  3212  of the illustrated example is hardware. For example, the processor  3212  can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer. The hardware processor  3212  may be a semiconductor based (e.g., silicon based) device. In this example, the processor  3212  implements one or more of the example agent interface  202 , the example agent local services  204 , the example analytics engine  206 , the example communication services  208 , the example agent CLI  210 , the example agent daemon  212 , the example license processor  214 , the example agent library  218 , the example feature libraries  220 - 230 , the example TEE identifier  1202 , the example TEE generator  1204 , the example TEE(s)  1205 , the example TEE library  1206 , the example feature intent determiner  1112 , and/or the example feature intent ML model(s)  1114 . 
     The processor  3212  of the illustrated example includes a local memory  3213  (e.g., a cache). The processor  3212  of the illustrated example is in communication with a main memory including a volatile memory  3214  and a non-volatile memory  3216  via a link  3218 . The link  3218  may be implemented by a bus, one or more point-to-point connections, etc., or a combination thereof. The volatile memory  3214  may be implemented by SDRAM, DRAM, RDRAM® and/or any other type of random access memory device. The non-volatile memory  3216  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  3214 ,  3216  is controlled by a memory controller. In this example, respective ones of the hardware circuitry  125 , the firmware  130 , and the BIOS  135  include the example TEE component(s)  1208 . 
     The processor platform  3200  of the illustrated example also includes an interface circuit  3220 . The interface circuit  3220  may be implemented by any type of interface standard, such as an Ethernet interface, a USB, a Bluetooth® interface, an NFC interface, and/or a PCI express interface. 
     In the illustrated example, one or more input devices  3222  are connected to the interface circuit  3220 . The input device(s)  3222  permit(s) a user to enter data and/or commands into the processor  3212 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, a trackbar (such as an isopoint), a voice recognition system and/or any other human-machine interface. Also, many systems, such as the processor platform  3200 , can allow the user to control the computer system and provide data to the computer using physical gestures, such as, but not limited to, hand or body movements, facial expressions, and face recognition. 
     One or more output devices  3224  are also connected to the interface circuit  3220  of the illustrated example. The output devices  3224  can be implemented, for example, by display devices (e.g., an LED, an OLED, an LCD, a CRT display, an IPS display, a touchscreen, etc.), a tactile output device, a printer and/or speakers(s). The interface circuit  3220  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip and/or a graphics driver processor. 
     The interface circuit  3220  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  3226 . The communication can be via, for example, an Ethernet connection, a DSL connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, etc. 
     The processor platform  3200  of the illustrated example also includes one or more mass storage devices  3228  for storing software and/or data. Examples of such mass storage devices  3228  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, RAID systems, and DVD drives. 
     The machine executable instructions  3232  corresponding to the instructions of  FIG.  19 - 22  and/or  25    may be stored in the mass storage device  3228 , in the volatile memory  3214 , in the non-volatile memory  3216 , in the local memory  3213  and/or on a removable non-transitory computer readable storage medium, such as a CD or DVD  3236 . 
       FIG.  33    is a block diagram of an example processor platform  3300  structured to execute the instructions of  FIGS.  26 - 27    to implement the time calculator  1302  and feature group calculator  1304  of  FIGS.  13 - 15   . The processor platform  3300  can be, for example, a server, a personal computer, a workstation, a self-learning machine (e.g., a neural network), a mobile device (e.g., a cell phone, a smart phone, a tablet such as an iPad™), a personal digital assistant (PDA), an Internet appliance, a DVD player, a CD player, a Blu-ray player, a gaming console, a personal video recorder, a headset or other wearable device, or any other type of computing device. 
     The processor platform  3300  of the illustrated example includes a processor  3312 . The processor  3312  of the illustrated example is hardware. For example, the processor  3312  can be implemented by one or more integrated circuits, logic circuits, microprocessors, GPUs, DSPs, or controllers from any desired family or manufacturer. The hardware processor may be a semiconductor based (e.g., silicon based) device. In this example, the processor  3312  implements the example time calculator  1302 , the example feature group calculator  1304 , the example request interface  1404 , the example property checker  1406 , the relative time determiner  1408 , the absolute determiner  1410 , the example configuration detector  1502 , the example sensor interface  1504 , the example environmental condition determiner  1506 , the example feature weight determiner  1508 , the example group score calculator  1510 , the example threshold comparator  1514 , and/or the example configuration controller  1516 . 
     The processor  3312  of the illustrated example includes a local memory  3313  (e.g., a cache). The processor  3312  of the illustrated example is in communication with a main memory including a volatile memory  3314  and a non-volatile memory  3316  via a bus  3318 . The volatile memory  3314  may be implemented by Synchronous Dynamic Random Access Memory (SDRAM), Dynamic Random Access Memory (DRAM), RAIVIBUS® Dynamic Random Access Memory (RDRAM®) and/or any other type of random access memory device. The non-volatile memory  3316  may be implemented by flash memory and/or any other desired type of memory device. Access to the main memory  3314 ,  3316  is controlled by a memory controller. 
     The processor platform  3300  of the illustrated example also includes an interface circuit  3320 . The interface circuit  3320  may be implemented by any type of interface standard, such as an Ethernet interface, a universal serial bus (USB), a Bluetooth® interface, a near field communication (NFC) interface, and/or a PCI express interface. 
     In the illustrated example, one or more input devices  3322  are connected to the interface circuit  3320 . The input device(s)  3322  permit(s) a user to enter data and/or commands into the processor  3312 . The input device(s) can be implemented by, for example, an audio sensor, a microphone, a camera (still or video), a keyboard, a button, a mouse, a touchscreen, a track-pad, a trackball, isopoint and/or a voice recognition system. 
     One or more output devices  3324  are also connected to the interface circuit  3320  of the illustrated example. The output devices  3324  can be implemented, for example, by display devices (e.g., a light emitting diode (LED), an organic light emitting diode (OLED), a liquid crystal display (LCD), a cathode ray tube display (CRT), an in-place switching (IPS) display, a touchscreen, etc.), a tactile output device, a printer and/or speaker. The interface circuit  3320  of the illustrated example, thus, typically includes a graphics driver card, a graphics driver chip and/or a graphics driver processor. 
     The interface circuit  3320  of the illustrated example also includes a communication device such as a transmitter, a receiver, a transceiver, a modem, a residential gateway, a wireless access point, and/or a network interface to facilitate exchange of data with external machines (e.g., computing devices of any kind) via a network  3326 . The communication can be via, for example, an Ethernet connection, a digital subscriber line (DSL) connection, a telephone line connection, a coaxial cable system, a satellite system, a line-of-site wireless system, a cellular telephone system, etc. 
     The processor platform  3300  of the illustrated example also includes one or more mass storage devices  3328  for storing software and/or data. Examples of such mass storage devices  3328  include floppy disk drives, hard drive disks, compact disk drives, Blu-ray disk drives, redundant array of independent disks (RAID) systems, and digital versatile disk (DVD) drives. 
     The machine executable instructions  3332  of  FIGS.  26  and  27    may be stored in the mass storage device  3328 , in the volatile memory  3314 , in the non-volatile memory  3316 , and/or on a removable non-transitory computer readable storage medium such as a CD or DVD. 
     A block diagram illustrating an example software distribution platform  3405  to distribute software such as the example computer readable instructions  2832 ,  2932 ,  3032 ,  3132 ,  3232  and/or  3332  of  FIGS.  28 - 30   ,  FIGS.  31 - 32    and  FIG.  33    to third parties is illustrated in  FIG.  34   . The example software distribution platform  3405  may be implemented by any computer server, data facility, cloud service, etc., capable of storing and transmitting software to other computing devices. The third parties may be customers of the entity owning and/or operating the software distribution platform. For example, the entity that owns and/or operates the software distribution platform may be a developer, a seller, and/or a licensor of software such as the example computer readable instructions  2832 ,  2932 ,  3032 ,  3132 ,  3232  and/or  3332  of  FIGS.  28 - 30   ,  FIGS.  31 - 32    and  FIG.  33   . The third parties may be consumers, users, retailers, OEMs, etc., who purchase and/or license the software for use and/or re-sale and/or sub-licensing. In the illustrated example, the software distribution platform  3405  includes one or more servers and one or more storage devices. The storage devices store the computer readable instructions  2832 ,  2932 ,  3032 ,  3132 ,  3232  and/or  3332 , which may correspond to the example computer readable instructions  1600 ,  1700 ,  1800 ,  1900 ,  2000 ,  2100 ,  2200 ,  2300 ,  2400 ,  2500 ,  2600  and/or  2700  of  FIGS.  16 - 18   ,  FIGS.  19 - 25    and  FIGS.  26 - 27   , as described above. The one or more servers of the example software distribution platform  3405  are in communication with a network  3410 , which may correspond to any one or more of the Internet and/or any of the example networks described above. In some examples, the one or more servers are responsive to requests to transmit the software to a requesting party as part of a commercial transaction. Payment for the delivery, sale and/or license of the software may be handled by the one or more servers of the software distribution platform and/or via a third party payment entity. The servers enable purchasers and/or licensors to download the computer readable instructions  2832 ,  2932 ,  3032 ,  3132 ,  3232  and/or  3332  from the software distribution platform  3405 . For example, the software, which may correspond to the example computer readable instructions  1600 ,  1700 ,  1800 ,  1900 ,  2000 ,  2100 ,  2200 ,  2300 ,  2400 ,  2500 ,  2600  and/or  2700  of  FIGS.  16 - 18   ,  FIGS.  19 - 25    and  FIGS.  26 - 27   , may be downloaded to the example processor platforms  2800 ,  2900 ,  3000 ,  3100 ,  3200  and/or  3300 , which execute the computer readable instructions  2832 ,  2932 ,  3032 ,  3132 ,  3232  and/or  3332  to implement the manufacture enterprise system  110 , the customer enterprise system  115 , the SDSi asset agent  140 , the SDSi asset agent  140 A-C, the time calculator  1302  and/or feature group calculator  1304 . In some example, one or more servers of the software distribution platform  3405  periodically offer, transmit, and/or force updates to the software (e.g., the example computer readable instructions  2832 ,  2932 ,  3032 ,  3132 ,  3232  and/or  3332  of  FIGS.  28 - 30 ,  31 - 32  and  33   ) to ensure improvements, patches, updates, etc. are distributed and applied to the software at the end user devices. 
     Edge Computing 
       FIG.  35    is a block diagram  3500  showing an overview of a configuration for edge computing, which includes a layer of processing referred to in many of the following examples as an “edge cloud”. As shown, the edge cloud  3510  is co-located at an edge location, such as an access point or base station  3540 , a local processing hub  3550 , or a central office  3520 , and thus may include multiple entities, devices, and equipment instances. The edge cloud  3510  is located much closer to the endpoint (consumer and producer) data sources  3560  (e.g., autonomous vehicles  3561 , user equipment  3562 , business and industrial equipment  3563 , video capture devices  3564 , drones  3565 , smart cities and building devices  3566 , sensors and IoT devices  3567 , etc.) than the cloud data center  3530 . Compute, memory, and storage resources which are offered at the edges in the edge cloud  3510  are critical to providing ultra-low latency response times for services and functions used by the endpoint data sources  3560  as well as reduce network backhaul traffic from the edge cloud  3510  toward cloud data center  3530  thus improving energy consumption and overall network usages among other benefits. 
     Compute, memory, and storage are scarce resources, and generally decrease depending on the edge location (e.g., fewer processing resources being available at consumer endpoint devices, than at a base station, than at a central office). However, the closer that the edge location is to the endpoint (e.g., user equipment (UE)), the more that space and power is often constrained. Thus, edge computing attempts to reduce the amount of resources needed for network services, through the distribution of more resources which are located closer both geographically and in network access time. In this manner, edge computing attempts to bring the compute resources to the workload data where appropriate, or, bring the workload data to the compute resources. 
     The following describes aspects of an edge cloud architecture that covers multiple potential deployments and addresses restrictions that some network operators or service providers may have in their own infrastructures. These include, variation of configurations based on the edge location (because edges at a base station level, for instance, may have more constrained performance and capabilities in a multi-tenant scenario); configurations based on the type of compute, memory, storage, fabric, acceleration, or like resources available to edge locations, tiers of locations, or groups of locations; the service, security, and management and orchestration capabilities; and related objectives to achieve usability and performance of end services. These deployments may accomplish processing in network layers that may be considered as “near edge”, “close edge”, “local edge”, “middle edge”, or “far edge” layers, depending on latency, distance, and timing characteristics. 
     Edge computing is a developing paradigm where computing is performed at or closer to the “edge” of a network, typically through the use of a compute platform (e.g., x86 or ARM compute hardware architecture) implemented at base stations, gateways, network routers, or other devices which are much closer to endpoint devices producing and consuming the data. For example, edge gateway servers may be equipped with pools of memory and storage resources to perform computation in real-time for low latency use-cases (e.g., autonomous driving or video surveillance) for connected client devices. Or as an example, base stations may be augmented with compute and acceleration resources to directly process service workloads for connected user equipment, without further communicating data via backhaul networks. Or as another example, central office network management hardware may be replaced with standardized compute hardware that performs virtualized network functions and offers compute resources for the execution of services and consumer functions for connected devices. Within edge computing networks, there may be scenarios in services which the compute resource will be “moved” to the data, as well as scenarios in which the data will be “moved” to the compute resource. Or as an example, base station compute, acceleration and network resources can provide services in order to scale to workload demands on an as needed basis by activating dormant capacity (subscription, capacity on demand) in order to manage corner cases, emergencies or to provide longevity for deployed resources over a significantly longer implemented lifecycle. 
       FIG.  36    illustrates operational layers among endpoints, an edge cloud, and cloud computing environments. Specifically,  FIG.  36    depicts examples of computational use cases  3605 , utilizing the edge cloud  3510  among multiple illustrative layers of network computing. The layers begin at an endpoint (devices and things) layer  3600 , which accesses the edge cloud  3510  to conduct data creation, analysis, and data consumption activities. The edge cloud  3510  may span multiple network layers, such as an edge devices layer  3610  having gateways, on-premise servers, or network equipment (nodes  3615 ) located in physically proximate edge systems; a network access layer  3620 , encompassing base stations, radio processing units, network hubs, regional data centers (DC), or local network equipment (equipment  3625 ); and any equipment, devices, or nodes located therebetween (in layer  3612 , not illustrated in detail). The network communications within the edge cloud  3510  and among the various layers may occur via any number of wired or wireless mediums, including via connectivity architectures and technologies not depicted. 
     Examples of latency, resulting from network communication distance and processing time constraints, may range from less than a millisecond (ms) when among the endpoint layer  3600 , under 5 ms at the edge devices layer  3610 , to even between 10 to 40 ms when communicating with nodes at the network access layer  3620 . Beyond the edge cloud  3510  are core network  3630  and cloud data center  3640  layers, each with increasing latency (e.g., between 50-60 ms at the core network layer  3630 , to 100 or more ms at the cloud data center layer). As a result, operations at a core network data center  3635  or a cloud data center  3645 , with latencies of at least 50 to 100 ms or more, will not be able to accomplish many time-critical functions of the use cases  3605 . Each of these latency values are provided for purposes of illustration and contrast; it will be understood that the use of other access network mediums and technologies may further reduce the latencies. In some examples, respective portions of the network may be categorized as “close edge”, “local edge”, “near edge”, “middle edge”, or “far edge” layers, relative to a network source and destination. For instance, from the perspective of the core network data center  3635  or a cloud data center  3645 , a central office or content data network may be considered as being located within a “near edge” layer (“near” to the cloud, having high latency values when communicating with the devices and endpoints of the use cases  3605 ), whereas an access point, base station, on-premise server, or network gateway may be considered as located within a “far edge” layer (“far” from the cloud, having low latency values when communicating with the devices and endpoints of the use cases  3605 ). It will be understood that other categorizations of a particular network layer as constituting a “close”, “local”, “near”, “middle”, or “far” edge may be based on latency, distance, number of network hops, or other measurable characteristics, as measured from a source in any of the network layers  3600 - 3640 . 
     The various use cases  3605  may access resources under usage pressure from incoming streams, due to multiple services utilizing the edge cloud. To achieve results with low latency, the services executed within the edge cloud  3510  balance varying requirements in terms of: (a) Priority (throughput or latency) and Quality of Service (QoS) (e.g., traffic for an autonomous car may have higher priority than a temperature sensor in terms of response time requirement; or, a performance sensitivity/bottleneck may exist at a compute/accelerator, memory, storage, or network resource, depending on the application); (b) Reliability and Resiliency (e.g., some input streams need to be acted upon and the traffic routed with mission-critical reliability, where as some other input streams may be tolerate an occasional failure, depending on the application); and (c) Physical constraints (e.g., power, cooling and form-factor). 
     The end-to-end service view for these use cases involves the concept of a service-flow and is associated with a transaction. The transaction details the overall service requirement for the entity consuming the service, as well as the associated services for the resources, workloads, workflows, and business functional and business level requirements. The services executed with the “terms” described may be managed at each layer in a way to assure real time, and runtime contractual compliance for the transaction during the lifecycle of the service. When a component in the transaction is missing its agreed to SLA, the system as a whole (components in the transaction) may provide the ability to (1) understand the impact of the SLA violation, and (2) augment other components in the system to resume overall transaction SLA, and (3) implement steps to remediate. 
     Thus, with these variations and service features in mind, edge computing within the edge cloud  3510  may provide the ability to serve and respond to multiple applications of the use cases  3605  (e.g., object tracking, video surveillance, connected cars, etc.) in real-time or near real-time, and meet ultra-low latency requirements for these multiple applications. These advantages enable a whole new class of applications (Virtual Network Functions (VNFs), Function as a Service (FaaS), Edge as a Service (EaaS), standard processes, etc.), which cannot leverage conventional cloud computing due to latency or other limitations. 
     However, with the advantages of edge computing comes the following caveats. The devices located at the edge are often resource constrained and therefore there is pressure on usage of edge resources. Typically, this is addressed through the pooling of memory and storage resources for use by multiple users (tenants) and devices. The edge may be power and cooling constrained and therefore the power usage needs to be accounted for by the applications that are consuming the most power. There may be inherent power-performance tradeoffs in these pooled memory resources, as many of them are likely to use emerging memory technologies, where more power requires greater memory bandwidth. Likewise, improved security of hardware and root of trust trusted functions are also required, because edge locations may be unmanned and may even need permissioned access (e.g., when housed in a third-party location). Such issues are magnified in the edge cloud  3510  in a multi-tenant, multi-owner, or multi-access setting, where services and applications are requested by many users, especially as network usage dynamically fluctuates and the composition of the multiple stakeholders, use cases, and services changes. 
     At a more generic level, an edge computing system may be described to encompass any number of deployments at the previously discussed layers operating in the edge cloud  3510  (network layers  3600 - 4740 ), which provide coordination from client and distributed computing devices. One or more edge gateway nodes, one or more edge aggregation nodes, and one or more core data centers may be distributed across layers of the network to provide an implementation of the edge computing system by or on behalf of a telecommunication service provider (“telco”, or “TSP”), internet-of-things service provider, cloud service provider (CSP), enterprise entity, or any other number of entities. Various implementations and configurations of the edge computing system may be provided dynamically, such as when orchestrated to meet service objectives. 
     Consistent with the examples provided herein, a client compute node may be embodied as any type of endpoint component, device, appliance, or other thing capable of communicating as a producer or consumer of data. Further, the label “node” or “device” as used in the edge computing system does not necessarily mean that such node or device operates in a client or agent/minion/follower role; rather, any of the nodes or devices in the edge computing system refer to individual entities, nodes, or subsystems which include discrete or connected hardware or software configurations to facilitate or use the edge cloud  3510 . 
     As such, the edge cloud  3510  is formed from network components and functional features operated by and within edge gateway nodes, edge aggregation nodes, or other edge compute nodes among network layers  3610 - 4730 . The edge cloud  3510  thus may be embodied as any type of network that provides edge computing and/or storage resources which are proximately located to radio access network (RAN) capable endpoint devices (e.g., mobile computing devices, IoT devices, smart devices, etc.), which are discussed herein. In other words, the edge cloud  3510  may be envisioned as an “edge” which connects the endpoint devices and traditional network access points that serve as an ingress point into service provider core networks, including mobile carrier networks (e.g., Global System for Mobile Communications (GSM) networks, Long-Term Evolution (LTE) networks, 5G/6G networks, etc.), while also providing storage and/or compute capabilities. Other types and forms of network access (e.g., Wi-Fi, long-range wireless, wired networks including optical networks) may also be utilized in place of or in combination with such 3GPP carrier networks. 
     The network components of the edge cloud  3510  may be servers, multi-tenant servers, appliance computing devices, and/or any other type of computing devices. For example, the edge cloud  3510  may include an appliance computing device that is a self-contained electronic device including a housing, a chassis, a case or a shell. In some circumstances, the housing may be dimensioned for portability such that it can be carried by a human and/or shipped. Example housings may include materials that form one or more exterior surfaces that partially or fully protect contents of the appliance, in which protection may include weather protection, hazardous environment protection (e.g., EMI, vibration, extreme temperatures), and/or enable submergibility. Example housings may include power circuitry to provide power for stationary and/or portable implementations, such as AC power inputs, DC power inputs, AC/DC or DC/AC converter(s), power regulators, transformers, charging circuitry, batteries, wired inputs and/or wireless power inputs. Example housings and/or surfaces thereof may include or connect to mounting hardware to enable attachment to structures such as buildings, telecommunication structures (e.g., poles, antenna structures, etc.) and/or racks (e.g., server racks, blade mounts, etc.). Example housings and/or surfaces thereof may support one or more sensors (e.g., temperature sensors, vibration sensors, light sensors, acoustic sensors, capacitive sensors, proximity sensors, etc.). One or more such sensors may be contained in, carried by, or otherwise embedded in the surface and/or mounted to the surface of the appliance. Example housings and/or surfaces thereof may support mechanical connectivity, such as propulsion hardware (e.g., wheels, propellers, etc.) and/or articulating hardware (e.g., robot arms, pivotable appendages, etc.). In some circumstances, the sensors may include any type of input devices such as user interface hardware (e.g., buttons, switches, dials, sliders, etc.). In some circumstances, example housings include output devices contained in, carried by, embedded therein and/or attached thereto. Output devices may include displays, touchscreens, lights, LEDs, speakers, I/O ports (e.g., USB), etc. In some circumstances, edge devices are devices presented in the network for a specific purpose (e.g., a traffic light), but may have processing and/or other capacities that may be utilized for other purposes. Such edge devices may be independent from other networked devices and may be provided with a housing having a form factor suitable for its primary purpose; yet be available for other compute tasks that do not interfere with its primary task. Edge devices include Internet of Things devices. The appliance computing device may include hardware and software components to manage local issues such as device temperature, vibration, resource utilization, updates, power issues, physical and network security, etc. The example processor systems of  FIGS.  28  to  33    illustrate example hardware for implementing an appliance computing device. The edge cloud  3510  may also include one or more servers and/or one or more multi-tenant servers. Such a server may include an operating system and a virtual computing environment. A virtual computing environment may include a hypervisor managing (spawning, deploying, destroying, etc.) one or more virtual machines, one or more containers, etc. Such virtual computing environments provide an execution environment in which one or more applications and/or other software, code or scripts may execute while being isolated from one or more other applications, software, code or scripts. 
     In  FIG.  37   , various client endpoints  3710  (in the form of mobile devices, computers, autonomous vehicles, business computing equipment, industrial processing equipment) exchange requests and responses that are specific to the type of endpoint network aggregation. For instance, client endpoints  3710  may obtain network access via a wired broadband network, by exchanging requests and responses  3722  through an on-premise network system  3732 . Some client endpoints  3710 , such as mobile computing devices, may obtain network access via a wireless broadband network, by exchanging requests and responses  3724  through an access point (e.g., cellular network tower)  3734 . Some client endpoints  3710 , such as autonomous vehicles may obtain network access for requests and responses  3726  via a wireless vehicular network through a street-located network system  3736 . However, regardless of the type of network access, the TSP may deploy aggregation points  3742 ,  3744  within the edge cloud  3510  to aggregate traffic and requests. Thus, within the edge cloud  3510 , the TSP may deploy various compute and storage resources, such as at edge aggregation nodes  3740 , to provide requested content. The edge aggregation nodes  3740  and other systems of the edge cloud  3510  are connected to a cloud or data center  3760 , which uses a backhaul network  3750  to fulfill higher-latency requests from a cloud/data center for websites, applications, database servers, etc. Additional or consolidated instances of the edge aggregation nodes  3740  and the aggregation points  3742 ,  3744 , including those deployed on a single server framework, may also be present within the edge cloud  3510  or other areas of the TSP infrastructure. 
     CONCLUSION 
     From the foregoing, it will be appreciated that example methods, apparatus and articles of manufacture have been disclosed that enables activation, deactivation and management of silicon product features after the silicon product has left the manufacturer&#39;s facility and control. The disclosed methods, apparatus and articles of manufacture improve the efficiency of using a computing device by providing mechanisms to activate dormant features of the silicon product, deactivate active features of the silicon product, perform failure recovery, etc. The disclosed methods, apparatus and articles of manufacture are accordingly directed to one or more improvement(s) in the functioning of a computer. 
     From the foregoing, it will also be appreciated that example methods, apparatus, and articles of manufacture have been disclosed that effectuate security of silicon product features after the silicon product has left the manufacturer&#39;s facility and control. The disclosed methods, apparatus, and articles of manufacture improve the efficiency of using a computing device by providing mechanisms to activate dormant features of the silicon product, deactivate active features of the silicon product, perform failure recovery, etc., corresponding to mesh attestation processes, deployment of TEE(s), and translation of intent(s) or intended outcome(s) associated with configuration change requests into dormant features of which to activate. The disclosed methods, apparatus and articles of manufacture are accordingly directed to one or more improvement(s) in the functioning of a computer. 
     From the foregoing, it will also be appreciated that example methods, apparatus, and articles of manufacture disclosed herein improve the efficiency of using a computing device by providing mechanisms to activate dormant features of the silicon product, deactivate active features of the silicon product, perform failure recovery, etc., corresponding to an unfalsifiable and reliable method of determining time references and determining feature load on a processor. The disclosed methods, apparatus and articles of manufacture are accordingly directed to one or more improvement(s) in the functioning of a computer. 
     Example methods, apparatus, systems, and articles of manufacture to provide device enhancements for software defined silicon implementations are disclosed herein. Further examples and combinations thereof include the following: 
     Example 1 includes an apparatus comprising a request interface to receive a request for a timestamp, a property checker to determine a first value of an electrical property of a feature embedded in a silicon product, the feature having electrical properties that change over time, and a relative time determiner to calculate a relative time between the request and a previous event based on the first value of the electrical property and a second value of the electrical property, the second value of the electrical property associated with the previous event. 
     Example 2 optionally includes the apparatus of example 1, wherein the feature includes a radioisotope. 
     Example 3 optionally includes the apparatus of example 1, wherein the feature includes a physical unclonable function. 
     Example 4 optionally includes the apparatus of any of examples 1-3, wherein the previous event is a time of manufacture of the silicon product. 
     Example 5 optionally includes the apparatus of any of examples 1-4, further including an absolute time determiner to calculate an absolute time based on the relative time and a recorded time of the previous event. 
     Example 6 optionally includes the apparatus of any of examples 1-5, wherein the electrical property is at least one of a resistance of the feature or a capacitance of the feature. 
     Example 7 optionally includes the apparatus of any of examples 1-6, wherein the request is issued by a client enterprise system, the request issued in association with a determination of whether to disable a license. 
     Example 8 includes a method comprising receiving a request for a timestamp, determining a first value of an electrical property of a feature embedded in a silicon product, the feature having electrical properties that change over time, and calculating a relative time between the request and a previous event based on the first value of the electrical property and a second value of the electrical property, the second value of the electrical property associated with the previous event. 
     Example 9 optionally includes the method of example 8, wherein the feature includes a radioisotope. 
     Example 10 optionally includes the method of example 8, wherein the feature includes a physical unclonable function. 
     Example 11 optionally includes the method of any of examples 8-10, wherein the previous event is a time of manufacture of the silicon product. 
     Example 12 optionally includes the method of any of examples 8-11, further including calculating an absolute time based on the relative time and a recorded time of the previous event. 
     Example 13 optionally includes the method of any of examples 8-12, wherein the electrical property is at least one of a resistance of the feature or a capacitance of the feature. 
     Example 14 optionally includes the method of any of examples 8-13, wherein the request is issued by a client enterprise system, the request issued in association with a determination of whether to disable a license. 
     Example 15 includes a non-transitory computer readable medium, comprising instructions, which when executed, cause a machine to receive a request for a timestamp, determine a first value of an electrical property of a feature embedded in a silicon product, the feature having electrical properties that change over time, and calculate a relative time between the request and a previous event based on the first value of the electrical property and a second value of the electrical property, the second value of the electrical property associated with the previous event. 
     Example 16 optionally includes the non-transitory computer readable medium of example 15, wherein the feature includes a radioisotope. 
     Example 17 optionally includes the non-transitory computer readable medium of example 15, wherein the feature includes a physical unclonable function. 
     Example 18 optionally includes the non-transitory computer readable medium of any of examples 15-17, wherein the previous event is a time of manufacture of the silicon product. 
     Example 19 optionally includes the non-transitory computer readable medium of any of examples 15-18, wherein the instructions further cause the machine to calculate an absolute time based on the relative time and a recorded time of the previous event. 
     Example 20 optionally includes the non-transitory computer readable medium of any of examples 15-19, wherein the electrical property is at least one of a resistance of the feature or a capacitance of the feature. 
     Example 21 optionally includes the non-transitory computer readable medium of any of examples 15-20, wherein the request is issued by a client enterprise system, the request issued in association with a determination of whether to disable a license. 
     Example 22 includes an apparatus comprising a configuration detector to detect a configuration change of a silicon product, the configuration change including a first feature and a second feature, a feature weight determiner to determine a first weight associated with the first feature and a second weight associated the second feature, a group score calculator to calculate a first group score based on the first weight and the second weight, and a configuration controller to disable the configuration change if the first group score does not satisfy a first threshold. 
     Example 23 optionally includes the apparatus of example 22, further including an environmental condition determiner the first group score to determine an environmental score based on an environmental condition of the silicon product, further based on the environmental score. 
     Example 24 optionally includes the apparatus of examples 23, wherein the environmental score is based on at least one of an ambient temperature, an ambient humidity, or a radiation. 
     Example 25 optionally includes the apparatus of any of examples 23-24, wherein the environmental score scales linearly relative to the ambient temperature. 
     Example 26 optionally includes the apparatus of any of examples 22-25, wherein a first feature group includes the first feature and the second feature and a second feature group includes a third feature and a fourth feature, and further including the feature weight determiner to determine a third weight associated with the third feature and a fourth weight associated the fourth feature, the group score calculator to calculate a second group score based on the third weight and the fourth weight, and the configuration controller to disable the configuration change if the second group score does not satisfy a second threshold, the second threshold associated with the second feature group, the first threshold associated with the second feature group. 
     Example 27 optionally includes the apparatus of any of examples 22-26, wherein the first feature is a number of active processor cores and the second feature is an operational frequency of the active processor cores. 
     Example 28 optionally includes the apparatus of any of examples 22-27, wherein the configuration controller is further to void a warranty of the silicon product if the first group score exceeds a second threshold. 
     Example 29 includes a non-transitory computer readable medium comprising instructions, which when executed, cause a machine to at least detect a configuration change of a silicon product, the configuration change including a first feature and a second feature, determine a first weight associated with the first feature and a second weight associated the second feature, calculate a first group score based on the first weight and the second weight, and disable the configuration change if the first group score does not satisfy a first threshold. 
     Example 30 optionally includes the non-transitory computer readable medium of example 29, wherein the instructions further cause the machine to determine an environmental score based on an environmental condition of the silicon product, the first group score further based on the environmental score. 
     Example 31 optionally includes the non-transitory computer readable medium of example 30, wherein the environmental score is based on at least one of an ambient temperature, an ambient humidity, or a radiation. 
     Example 32 optionally includes the non-transitory computer readable medium of any of examples 30-31, wherein the environmental score scales linearly relative to the ambient temperature. 
     Example 33 optionally includes the non-transitory computer readable medium of any of examples 29-32, wherein a first feature group includes the first feature and the second feature and a second feature group includes a third feature and a fourth feature, and the instructions further cause the machine to determine a third weight associated with the third feature and a fourth weight associated the fourth feature, calculate a second group score based on the third weight and the fourth weight, and disable the configuration change if the second group score does not satisfy a second threshold, the second threshold associated with the second feature group, the first threshold associated with the second feature group. 
     Example 34 optionally includes the non-transitory computer readable medium of any of examples 29-33, wherein the first feature is a number of active processor cores and the second feature is an operational frequency of the active processor cores. 
     Example 35 optionally includes the non-transitory computer readable medium of any of examples 29-34, wherein the instructions further cause the machine to void a warranty of the silicon product if the first group score exceeds a second threshold. 
     Example 36 includes a method comprising detecting a configuration change of a silicon product, the configuration change including a first feature and a second feature, determining a first weight associated with the first feature and a second weight associated the second feature, calculating a first group score based on the first weight and the second weight, and disabling the configuration change if the first group score does not satisfy a first threshold. 
     Example 37 optionally includes the method of example 36, further including determining an environmental score based on an environmental condition of the silicon product, the first group score further based on the environmental score. 
     Example 38 optionally includes the method of example 37, wherein the environmental score is based on at least one of an ambient temperature, an ambient humidity, or a radiation. 
     Example 39 optionally includes the method of any of examples 37-38, wherein the environmental score scales linearly relative to the ambient temperature. 
     Example 40 optionally includes the method of any of examples 36-39, wherein a first feature group includes the first feature and the second feature and a second feature group includes a third feature and a fourth feature, and further including determining a third weight associated with the third feature and a fourth weight associated the fourth feature, calculating a second group score based on the third weight and the fourth weight, and disabling the configuration change if the second group score does not satisfy a second threshold, the second threshold associated with the second feature group, the first threshold associated with the second feature group. 
     Example 41 optionally includes the method of any of examples 36-40, wherein the first feature is a number of active processor cores and the second feature is an operational frequency of the active processor cores. 
     Example 42 optionally includes the method of any of examples 36-41, further including voiding a warranty of the silicon product if the first group score exceeds a second threshold. 
     Example 43 includes an apparatus comprising means for receiving a request for a timestamp, means for determining a first value of an electrical property of a feature embedded in a silicon product, the feature having electrical properties that change over time, and means calculating a relative time between the request and a previous event based on the first value of the electrical property and a second value of the electrical property, the second value of the electrical property associated with the previous event. 
     Example 44 optionally includes the apparatus of example 43, wherein the feature includes a radioisotope. 
     Example 45 optionally includes the apparatus of example 43, wherein the feature includes a physical unclonable function. 
     Example 46 optionally includes the apparatus of any of examples 43-45, wherein the previous event is a time of manufacture of the silicon product. 
     Example 47 optionally includes the apparatus of any of examples 43-46, further including calculating an absolute time based on the relative time and a recorded time of the previous event. 
     Example 48 optionally includes the apparatus of any of examples 43-47, wherein the electrical property is at least one of a resistance of the feature or a capacitance of the feature. 
     Example 49 optionally includes the apparatus of any of examples 43-48, wherein the request is issued by a client enterprise system, the request issued in association with a determination of whether to disable a license. 
     Example 50 includes an apparatus comprising means for detecting a configuration change of a silicon product, the configuration change including a first feature and a second feature, means for first determining a first weight associated with the first feature and a second weight associated the second feature, means for calculating a first group score based on the first weight and the second weight, and means for disabling the configuration change if the first group score does not satisfy a first threshold. 
     Example 51 optionally includes the apparatus of example 50, further including second means for determining an environmental score based on an environmental condition of the silicon product, the first group score further based on the environmental score. 
     Example 52 optionally includes the apparatus of example 51, wherein the environmental score is based on at least one of an ambient temperature, an ambient humidity, or a radiation. 
     Example 53 optionally includes the apparatus of any of examples 51-52, wherein the environmental score scales linearly relative to the ambient temperature. 
     Example 54 optionally includes the apparatus of any of examples 50-53, wherein a first feature group includes the first feature and the second feature and a second feature group includes a third feature and a fourth feature, and further including the first means for determining to determine a third weight associated with the third feature and a fourth weight associated the fourth feature, the means for calculating to calculate a second group score based on the third weight and the fourth weight, and the means for disabling to disable the configuration change if the second group score does not satisfy a second threshold, the second threshold associated with the second feature group, the first threshold associated with the second feature group. 
     Example 55 optionally includes the apparatus of any of examples 50-54, wherein the first feature is a number of active processor cores and the second feature is an operational frequency of the active processor cores. 
     Example 56 optionally includes the apparatus of any of examples 50-55, wherein the means for disabling is to void a warranty of the silicon product if the first group score exceeds a second threshold. 
     Example 57 is an apparatus, comprising processing circuitry to perform any of Examples 8-14. 
     Example 58 is a computer-readable medium comprising instructions to perform any of Examples 8-14. 
     Example 59 is an apparatus, comprising processing circuitry to perform any of Examples 36-42. 
     Example 60 is a computer-readable medium comprising instructions to perform any of Examples 36-42. 
     Although certain example methods, apparatus and articles of manufacture have been disclosed herein, the scope of coverage of this patent is not limited thereto. On the contrary, this patent covers all methods, apparatus and articles of manufacture fairly falling within the scope of the claims of this patent. 
     The following claims are hereby incorporated into this Detailed Description by this reference, with each claim standing on its own as a separate embodiment of the present disclosure.