ARTIFICIAL INTELLIGENCE GOVERNED PROCESSOR

A method includes identifying tasks to be completed by an AI governed processing unit, monitoring performance metrics of the AI governed processing unit, training an AI governance engine to predict performance corresponding to the AI governed processing unit based on the monitored performance metrics and workload features corresponding to the identified tasks, determining whether a predicted performance metric according to the AI governance engine exceeds the monitored one or more performance metrics, and responsive to determining the predicted performance metric exceeds the monitored performance metrics, enabling the AI governance engine to optimize workload allocation relative to the identified tasks and the AI governed processing unit. A system includes an artificial intelligence (AI) governance engine, an AI governed processing pipeline configured to execute a set of tasks, and one or more performance monitors configured to monitor performance of the AI governed processing pipeline.

BACKGROUND OF THE INVENTION

The present invention relates generally to the field of processor architecture, and more specifically to increasing performance and efficiency in processors using machine learning techniques.

Typically, processors are implemented as pipeline architectures where instructions flow at each cycle using the available computational resources. Pipelined processors can be subject to structural hazards, wherein the resources must be free to be used when they are needed. Pipelined processor performance can additionally be impacted by data hazards; in other words, data must be available to be read or written for pipelined processors to proceed appropriately. Further, pipelined processors are subject to control hazards, such that the correct instructions must be fetched and ready to be executed at any given time to carry out execution. A considerable amount of design effort, power, and die area is focused towards minimizing the impact of these hazards. Such capabilities come at the cost of more area and energy invested in control structures rather than compute structures. Further, designers are required to include large amounts of on-chip memory to facilitate these efforts.

Advances in machine learning (ML) and artificial intelligence (AI) provide higher degrees of predictive capability that can be leveraged by microarchitectures to increase performance and energy efficiency. Machine learning/artificial intelligence models are effective for predicting dynamic temporal patterns (such as address sequences, for example). Current core and processor designs utilize a considerable amount of area (up to 50-60% in some cases) to control and data structures. The decisions made by such control structures, such as prioritization decisions, do not always adjust the workload, may in some cases be suboptimal, and can be subject to human biases and any existing deficiencies of “average” cases. In general, there doesn't exist a good hold for what data might be needed in the future, thus leaving performance on the table and wasting area.

Machine learning and artificial intelligence solutions are posed to solve classical computer architecture predictive problems such as prefetching, branch prediction, scheduling, and the like. At current, however, no implementations in the art exist which leverage an AI-governed processor. Creation of such an AI-governed processor is an opportunity to create a unified control inference engine capable of understanding workload definition and acting accordingly to save area and power while also increasing performance.

SUMMARY

Embodiments of the present invention disclose a computer-implemented method comprising identifying a set of tasks to be completed by an AI governed processing unit, monitoring performance metrics corresponding to the AI governed processing unit's performance while working on the set of tasks, training an AI governance engine to predict performance corresponding to the AI governed processing unit based on the monitored performance metrics and workload features corresponding to the identified set of tasks, determining whether a predicted performance metric according to the AI governance engine exceeds the monitored one or more performance metrics, and responsive to determining the predicted performance metric exceeds the monitored one or more performance metrics, enabling the AI governance engine to optimize workload allocation relative to the identified set of tasks and the AI governed processing unit. AI governance of a processing pipeline in this manner provides increased performance while also increasing efficiency in technical requirements.

In embodiments, enabling the AI governance engine to optimize workload allocation includes activating a setting corresponding to the AI governance engine such that said setting is in an “ON” position. Allowing the AI governance engine to be toggled to “ON” enables selective implementation of the AI governance engine, thus requiring the associated resources be utilized only when necessary.

In embodiments, enabling the AI governance engine to optimize workload allocation includes allocating tasks separately to different units of the AI governed processing unit. Allocating tasks separately in this manner allows tasks to be allocated to a unit of the AI governed processing unit best suited to carry out said task.

In embodiments, enabling the AI governance engine to optimize workload allocation includes enabling the AI governance engine for a subset of the set of tasks. Enabling the AI governance engine for a subset of the set of tasks increases the efficiency of the AI governance engine and overall execution by limiting the queue of tasks to be analyzed and managed via the AI governance engine while still reaping the benefits of increased efficiency and performance relative to the tasks managed by the AI governance engine.

In embodiments, enabling the AI governance engine to optimize workload allocation includes denying the AI governance engine access to a specified subset of the set of tasks. Denying the AI governance engine access to a specified subset of tasks allows selective exclusion of certain tasks, such as those where a client prefers private data not be exposed to the AI governance engine, etc.

Embodiments of the present invention disclose a computer program product comprising computer readable storage media storing instructions for identifying a set of tasks to be completed by an AI governed processing unit, monitoring performance metrics corresponding to the AI governed processing unit's performance while working on the set of tasks, training an AI governance engine to predict performance corresponding to the AI governed processing unit based on the monitored performance metrics and workload features corresponding to the identified set of tasks, determining whether a predicted performance metric according to the AI governance engine exceeds the monitored one or more performance metrics, and responsive to determining the predicted performance metric exceeds the monitored one or more performance metrics, enabling the AI governance engine to optimize workload allocation relative to the identified set of tasks and the AI governed processing unit. AI governance of a processing pipeline in this manner provides increased performance while also increasing efficiency in technical requirements.

In embodiments, program instructions for enabling the AI governance engine to optimize workload allocation include activating a setting corresponding to the AI governance engine such that said setting is in an “ON” position. Allowing the AI governance engine to be toggled to “ON” enables selective implementation of the AI governance engine, thus requiring the associated resources be utilized only when necessary.

In embodiments, program instructions for enabling the AI governance engine to optimize workload allocation include allocating tasks separately to different units of the AI governed processing unit. Allocating tasks separately in this manner allows tasks to be allocated to a unit of the AI governed processing unit best suited to carry out said task.

In embodiments, program instructions for enabling the AI governance engine to optimize workload allocation include enabling the AI governance engine for a subset of the set of tasks. Enabling the AI governance engine for a subset of the set of tasks increases the efficiency of the AI governance engine and overall execution by limiting the queue of tasks to be analyzed and managed via the AI governance engine while still reaping the benefits of increased efficiency and performance relative to the tasks managed by the AI governance engine.

In embodiments, program instructions for enabling the AI governance engine to optimize workload allocation include denying the AI governance engine access to a specified subset of the set of tasks. Denying the AI governance engine access to a specified subset of tasks allows selective exclusion of certain tasks, such as those where a client prefers private data not be exposed to the AI governance engine, etc.

Embodiments of the present invention disclose a computer program system comprising one or more computer processors and one or more computer readable storage media storing instructions for identifying a set of tasks to be completed by an AI governed processing unit, monitoring performance metrics corresponding to the AI governed processing unit's performance while working on the set of tasks, training an AI governance engine to predict performance corresponding to the AI governed processing unit based on the monitored performance metrics and workload features corresponding to the identified set of tasks, determining whether a predicted performance metric according to the AI governance engine exceeds the monitored one or more performance metrics, and responsive to determining the predicted performance metric exceeds the monitored one or more performance metrics, enabling the AI governance engine to optimize workload allocation relative to the identified set of tasks and the AI governed processing unit. AI governance of a processing pipeline in this manner provides increased performance while also increasing efficiency in technical requirements.

In embodiments, program instructions for enabling the AI governance engine to optimize workload allocation include activating a setting corresponding to the AI governance engine such that said setting is in an “ON” position. Allowing the AI governance engine to be toggled to “ON” enables selective implementation of the AI governance engine, thus requiring the associated resources be utilized only when necessary.

In embodiments, program instructions for enabling the AI governance engine to optimize workload allocation include allocating tasks separately to different units of the AI governed processing unit. Allocating tasks separately in this manner allows tasks to be allocated to a unit of the AI governed processing unit best suited to carry out said task.

In embodiments, program instructions for enabling the AI governance engine to optimize workload allocation include enabling the AI governance engine for a subset of the set of tasks. Enabling the AI governance engine for a subset of the set of tasks increases the efficiency of the AI governance engine and overall execution by limiting the queue of tasks to be analyzed and managed via the AI governance engine while still reaping the benefits of increased efficiency and performance relative to the tasks managed by the AI governance engine.

In embodiments, program instructions for enabling the AI governance engine to optimize workload allocation include denying the AI governance engine access to a specified subset of the set of tasks. Denying the AI governance engine access to a specified subset of tasks allows selective exclusion of certain tasks, such as those where a client prefers private data not be exposed to the AI governance engine, etc. An AI governed processing pipeline provides increased performance while also increasing efficiency in technical requirements.

Embodiments of the present invention disclose a system comprising an artificial intelligence (AI) governance engine, an AI governed processing pipeline configured to execute a set of one or more processing tasks, and one or more performance monitors configured to monitor performance of the AI governed processing pipeline. AI governance of a processing pipeline in this manner provides increased performance while also increasing efficiency in technical requirements.

In embodiments, the system further includes a sanity check unit configured to determine whether outputs of the AI governed processing pipeline adhere to one or more expected protocols. Utilizing a sanity check unit of this nature allows confirmation that the outputs of the AI governed processing pipeline make sense.

In embodiments, the system further includes a training module configured to process performance metrics as provided by the performance monitors and train the AI governance engine to infer performance outcomes based on the performance metrics as provided by the performance monitors and one or more features of the set of one or more processing tasks. Incorporating a training module of this nature enables informed inference of performance outcomes.

In embodiments, the AI governance engine is configured to predict a set of memory data which will be helpful for analyzing performance of the AI governed processing pipeline. Predicting a set of useful memory data enables increased efficiency when fetching such data.

In embodiments, the system further includes one or more memory buffers configured to store the predicted set of memory data. Incorporating memory buffers of this nature increases efficiency of the data fetch by ensuring useful memory data is stored in a consistent location.

Embodiments of the present invention disclose a method comprising receiving one or more performance metrics corresponding to an artificial intelligence governed processing pipeline, training an artificial intelligence governance engine to predict performance outcomes based on performance metrics, and determining an optimal resource allocation relative to a selected workload based on the trained AI governance engine. AI governance of a processing pipeline in this manner provides increased performance while also increasing efficiency in technical requirements.

In embodiments, the method further includes determining whether the predicted performance outcomes of the AI governed processing pipeline adhere to one or more expected protocols. Utilizing a sanity check of this nature allows confirmation that the outputs of the AI governed processing pipeline make sense.

In embodiments, the method further includes processing performance metrics as provided by one or more performance monitors and training the AI governance engine to infer performance outcomes based on the performance metrics as provided by the performance monitors and one or more features of the selected workload. Incorporating a training module of this nature enables informed inference of performance outcomes.

In embodiments, the AI governance engine is configured to predict a set of memory data which will be helpful for analyzing performance of the AI governed processing pipeline. Predicting a set of useful memory data enables increased efficiency when fetching such data.

In embodiments, the method further includes fetching and storing the predicted set of memory data. Storing the predicted set of memory data in this manner increases efficiency of the data fetch by ensuring useful memory data is stored in a consistent location.

DETAILED DESCRIPTION

Embodiments as disclosed herein enable increases in AI engine accuracy which may allow increased use of AI for predictive computing. Embodiments as disclosed herein may enable unification of control structures; typically designed as separate instances, control and predictive structures are bundled together in a single engine that can learn and influence each other and thus unify decision making into a same structural unit. Embodiments as disclosed herein may increase area availability for compute units by unifying the decision and fetch engines and heuristics, thus increasing system performance. In general, embodiments as disclosed herein may enable an AI governance engine configured to control a processing pipeline such that the AI governance engine may manage, and thereby optimize, functionality of components of the processing pipeline relative to a running workload.

FIG. 1 is a functional block diagram depicting an AI-governance pipeline 100 in accordance with at least one embodiment of the present invention. As depicted, AI-governance pipeline 100 includes AI governance engine 110, input memory buffers 120, performance monitors 130, output memory buffers 140, processing pipeline 150, sanity check unit 160, and control signals 170. AI-governance pipeline 100 may enable unified control inference and increased performance while also increasing efficiency in power utilization and space requirements on a chip. In general, AI-governance pipeline 100 refers to a traditional pipelined processor, such as processing pipeline 150, interfaced with an AI-governance engine, such as AI governance engine 110. AI-governance pipeline 100 may enable an inference engine to control the datapath of the processor pipeline. In at least some embodiments, data and instructions are fetched under AI-governance demand.

The present invention may contain various accessible data sources that may include personal data, content, or information the user wishes not to be processed. Personal data includes personally identifying information or sensitive personal information as well as user information, such as tracking or geolocation information. Processing refers to any operation, automated or unautomated, or set of operations such as collecting, recording, organizing, structuring, storing, adapting, altering, retrieving, consulting, using, disclosing by transmission, dissemination, or otherwise making available, combining, restricting, erasing, or destroying personal data. An interface of AI-governance pipeline 100 enables the authorized and secure processing of personal data. An interface of AI-governance pipeline 100 provides informed consent, with notice of the collection of personal data, allowing the user to opt in or opt out of processing personal data. Consent can take several forms. Opt-in consent can impose on the user to take an affirmative action before personal data is processed. Alternatively, opt-out consent can impose on the user to take an affirmative action to prevent the processing of personal data before personal data is processed. AI-governance pipeline 100 provides information regarding personal data and the nature (e.g., type, scope, purpose, duration, etc.) of the processing. AI-governance pipeline 100 provides the user with copies of stored personal data. AI-governance pipeline 100 allows the correction or completion of incorrect or incomplete personal data. AI-governance pipeline 100 allows the immediate deletion of personal data.

AI governance engine 110 includes a memory fetch inference unit 112, a training engine 114, and a control inference unit 115. In some embodiments, AI-governance engine 110 receives as inputs a processor status and additional detailed monitoring data including, but not limited to, performance metrics and address usage. With respect to memory fetch inference unit 112 and control inference unit 115, an epoch is defined for enabling a granularity of inference. In at least some embodiments, the epoch is defined according to time units. In other embodiments, the epoch is defined according to a processor metric such as executed instructions. At the end of a given epoch, AI governance engine 110 is configured to issue a set of directives to be followed/carried out during the following epoch. Memory fetch inference 112 may be configured to fetch and store observed control signals, performance monitors, processor performance, and workload types. In general, control and processor status signals flow to the AI governance engine 110.

Memory fetch inference 112 may be configured to fetch and store observed control signals, performance monitors, processor performance, and workload types. Memory fetch interface 112 may be configured to predict a set of memory data that needs to be fetched into the input buffers 120, and extracted from the output buffers 130. In at least some embodiments, the predicted set of memory data that needs to be fetched may correspond to data which may prove helpful for analyzing performance of the processing pipeline 150.

Training engine 114 is configured to receive the raw input data and identify one or more correlations between an internal state of a processor of processing pipeline 150, the performance of said processor, and a type of workload corresponding to said workload. In at least some embodiments, training engine 114 is trained to generate data/data address, instruction addresses, control signals, and prioritization decisions for the pipeline that maximize performance and energy efficiency. In at least some embodiments, training engine 114 is trained to identify a workload given a set of observed control signals, fetches, and performance metrics for the processor pipeline 150.

Control inference 116 may be configured to issue control signals that are optimized to provide the best performance for a given workload being executed. Based on the control signals, performance monitors, processor performance, and workload type, control inference 116 may be configured to infer data/data addresses and instruction addresses. In general, control inference 116 is the part of the AI governance engine 110 that is in charge of generating datapath control signals. The datapath control signals may include, but are not limited to, which instructions must be executed and in what order, which threads are to be prioritized, and arbitration policy decisions such as access to shared busses.

Input memory buffers 120 include one or more temporary storage areas configured to hold data received from an input device. In at least some embodiments, such as the embodiment depicted, input memory buffers 120 are configured to hold data received from AI governance engine 110. The data received from AI governance engine 110 and held by input memory buffers 120 may include signals for controlling a datapath of the processor pipeline. In at least some embodiments, input memory buffers 120 are configured to hold data/instructions that are being used or are ready to be moved to internal registry files.

Performance monitors 130 may be configured to monitor the performance of processing pipeline 150 and its various components. In at least some embodiments, performance monitors 130 monitor metrics such as, but not limited to, IPC, decoded and committed instructions, loads, etc. In at least some embodiments, performance monitors 130 store or provide metrics in sequences, and feed these sequences to a reconfigurable fabric that filters out which metrics are used in the training process. The reconfigurable fabric may be trained by training engine 114, thus automatically learning key metrics. In other embodiments, the reconfigurable fabric is configured by human expertise.

Output memory buffers 140 include one or more temporary storage areas configured to hold data created by processing pipeline 150. In at least some embodiments, such as the embodiment depicted, output memory buffers 140 are accessible by (or transmit data to) AI governance engine 110 via input memory buffers 120. The data held by output memory buffers 140 may include processor status information. In general, AI governance engine 110 may infer/predict a need for data stored in output memory buffers 140, and will issue a request that said data be made available via input memory buffers 120.

Processing pipeline 150 includes front end 151, queue 152, compute units 153, memory interface 154, and backend 155. Front end 151 is an interface between a user and the application processes of processing pipeline 150. In at least some embodiments, front end 151 enables a user to enter data that is collected and queued, via queue 152, to be processed by compute units 153. Compute units 153 may be configured to process received data in such away that it conforms to what the back end 155 can accept and process. Memory interface 154 may be configured to store data processed by compute units 153 until backend 155 is available to receive said stored data.

Sanity check unit 160 is configured to evaluate control signals emanated from AI governance engine 110 to ensure program correctness. In at least some embodiments, signals that sanity check unit 160 determines to be incompatible are defaulted to a safe state that will ensure program correctness at the expense of performance as necessary.

Control signals 170 are signals issued by AI governance engine 110 configured to control operation execution within processing pipeline 150. Control signals 170 may be configured to fetch right data and instructions at a right time, prioritize execution thread, prioritize instructions, or invalidate or write-back data.

FIG. 2 is a dataflow diagram depicting a dispatching environment 200 in accordance with at least one embodiment of the present invention. As depicted, dispatching environment 200 includes AI governed processor unit 210, dispatcher 220, work unit pool 230, and work unit subset 240. As depicted, AI governed processing unit includes AI governance engine 212 and processor unit 214. It should be appreciated that processor unit 214 may not refer to a single processor, but rather to any arrangement of processors/processing units which are managed via the AIGP 210. The functions of the components of dispatching environment 200 are described with respect to dispatch method 300 in FIG. 3. Dispatching environment 200 may enable increased performance and efficiency relative to workload completion.

FIG. 3 is a flowchart depicting a dispatch method 300 in accordance with at least one embodiment of the present invention. As depicted, dispatch method 300 includes receiving (310) a set of work units, selecting (320) a subset of the set of work units, monitoring (330) the subset of work units for one or more epochs, training (340) an AI governed processing unit based on measured performance data from the monitored subset of work units, determining (350) whether the measured performance data exceeds predicted performance data, activating (360) the inference engines, and allocating (370) work units accordingly. Dispatch method 300 will be described with respect to dispatching environment 200 as depicted in FIG. 2. It should be appreciated that the term “work units” is used with respect to FIG. 3, and may be considered analogous to “tasks” or “sets of tasks” as referenced elsewhere.

Receiving (310) a set of work units includes a dispatcher, such as dispatcher 220, receiving a set of work units, such as work units 210, which it is responsible for managing. In at least some embodiments, receiving (310) a set of work units includes receiving authority to schedule the set of work units. Receiving (310) a set of work units may include receiving an indication that it is the responsibility of the dispatcher to allocate resources to complete said set of work units. In general, receiving (310) a set of work units includes receiving of a set of work units whose completion will be managed by a dispatcher 220.

Selecting (320) a subset of the set of work units may include identifying one or more work units of the set of work units which are to be scheduled to the AIGP 210. In at least some embodiments, selecting (320) a subset of the set of work units includes selecting work units which correspond to tasks as handled by the AIGP 210. For example, in a case where AIGP 210 is adept at handling certain operations based on its resource allocation, a subset of the set of work units may be chosen based on whether or not those work units correspond to said operations.

Monitoring (330) the subset of work units for one or more epochs may include monitoring the performance of the processor unit as it processes the subset of work units for a predetermined period of time. As described previously, the “epochs” for which the dispatcher may monitor the processor can correspond to a duration measured in units of time or in operational terms, such as the completion of a set number of operations, etc. In general, monitoring (330) the subset of work units for one or more epochs includes tracking the performance of the processor as it processes the subset of work units for the selected time period.

Training (340) an AI governed processing unit based on measured performance data from the monitored subset of work units may include feeding performance data to a neural model. An appropriate training methodology is described in greater detail with respect to FIG. 4.

Determining (350) whether the measured performance data exceeds predicted performance data may include comparing a calculated predicted performance metric to an observed measured performance metric. If the measured performance data exceeds the predicted performance data (350, yes branch), the method continues by returning to monitoring (330) the performance of the subset of work units for a selected number of additional epochs. In other words, if the measured performance is exceeding performance as predicted by AIGP 210, the method continues without activating the governance engine of AIGP 210. If the measured performance data does not exceed the predicted performance data (350, no branch), the method continues by activating (360) the inference engines.

Activating (360) the inference engines may include enabling the trained AI governance engine to determine an optimal resource utilization for the processor unit 214. In at least some embodiments, activating (360) the inference engines includes using the trained model of the AI governed processing unit to identify a most efficient utilization of the resources available via processor unit 214 relative to a current workload.

Allocating (370) work units accordingly may include allocating the subset of work units according to the optimal resource utilization as determined by the AI governed processing unit. In general, allocating (370) work units accordingly refers to allocating the work units to reflect the optimal allocation as determined by the AI governance engine. In general, the dispatcher can opt to allocate work units together or separately based on the optimal allocation, or can disable AI-governance for certain work units where it is either unnecessary or undesirable. Allocating (370) work units accordingly may additionally include receiving feedback reports from the AI governance engine indicating success of the AIGP's inferences and optimizations in terms of performance increase.

FIG. 4 is a functional block diagram depicting a training environment 400 in accordance with at least one embodiment of the present invention. As depicted, training environment 400 includes control features 410, performance features 420, control features 430, predicted performance 440, and neural model 450. Training environment 400 may enable a neural model of an AI governance engine to be trained based on control features and performance features of past epochs. The model may feed on a sequence and variety of inputs or features in both bit and float format. The features can be divided into two main types, control features 410 and performance features 420.

Control features 410 may include, but are not limited to, functional unit occupancy (bit), addresses (bit), and queue free and dispatched instructions (bit). Performance features 420 may include, but are not limited to, buffer hit rate (float) and IPC (float). Control features 410 and performance features 420 are concatenated and fed to neural model 450 within an AI governance engine. Neural model 450 may have different architectures (such as, but not limited to, deep neural networks (DNN), long short-term memory (LSTM), transformer, etc.), and can be a combination of several different approaches.

Output control features 430 correspond to control features as outputted from the neural model 450. Output control features 430 may include bit output such as data and instruction addresses to be fed to units in the AI governed processor. In at least some embodiments, output control features 430 include assignment of instructions to functional units. Output control features 430 may additionally include predicted data values. Output control features 430 may be checked by a sanity check unit. Predicted performance 440 as provided by the neural model 450 may include a predictor of performance for the following epoch.

In at least some embodiments, a switch may be implemented such that the AI governance engine may be switched off and on based on the comparison between the predicted performance and the measured performance. In other words, when the predicted performance exceeds the measured performance, the AI governance engine will either be turned on or will remain on; on the other hand, when the measured performance exceeds the predicted performance, the AI governance engine will either be turned off or will remain off.

FIG. 5 is an example diagram of a distributed data processing environment in which aspects of one or more of the illustrative embodiments may be implemented, and at least some of the computer code involved in performing the inventive methods may be executed, in accordance with an embodiment of the present invention, in accordance with an embodiment of the present invention. It should be appreciated that FIG. 5 provides only an illustration of one implementation and does not imply any limitations with regard to the environments in which different embodiments can be implemented. Many modifications to the depicted environment can be made.

Computing environment 500 contains an example of an environment for the execution of at least some of the computer code involved in performing the inventive methods, such as AI governance engine 110. In addition to AI governance engine 110, computing environment 500 includes, for example, computer 501, wide area network (WAN) 502, end user device (EUD) 503, remote server 504, public cloud 505, and private cloud 506. In this embodiment, computer 501 includes processor set 510 (including processing circuitry 520 and cache 521), communication fabric 511, volatile memory 512, persistent storage 513 (including operating system 522 and AI governance engine 110, as identified above), peripheral device set 514 (including user interface (UI), device set 523, storage 524, and Internet of Things (IoT) sensor set 525), and network module 515. Remote server 504 includes remote database 530. Public cloud 505 includes gateway 540, cloud orchestration module 541, host physical machine set 542, virtual machine set 543, and container set 544.

Processor set 510 includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry 520 may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry 520 may implement multiple processor threads and/or multiple processor cores. Cache 521 is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set 510. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor set 510 may be designed for working with qubits and performing quantum computing.

Computer readable program instructions are typically loaded onto computer 501 to cause a series of operational steps to be performed by processor set 510 of computer 501 and thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cache 521 and the other storage media discussed below. The program instructions, and associated data, are accessed by processor set 510 to control and direct performance of the inventive methods. In computing environment 500, at least some of the instructions for performing the inventive methods may be stored in AI governance engine 110 in persistent storage 513.

Volatile memory 512 is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, the volatile memory is characterized by random access, but this is not required unless affirmatively indicated. In computer 501, the volatile memory 512 is located in a single package and is internal to computer 501, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer 501.

Network module 515 is the collection of computer software, hardware, and firmware that allows computer 501 to communicate with other computers through WAN 502. Network module 515 may include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network module 515 are performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network module 515 are performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer readable program instructions for performing the inventive methods can typically be downloaded to computer 501 from an external computer or external storage device through a network adapter card or network interface included in network module 515.

End user device (EUD) 503 is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer 501) and may take any of the forms discussed above in connection with computer 501. EUD 503 typically receives helpful and useful data from the operations of computer 501. For example, in a hypothetical case where computer 501 is designed to provide a recommendation to an end user, this recommendation would typically be communicated from network module 515 of computer 501 through WAN 502 to EUD 503. In this way, EUD 503 can display, or otherwise present, the recommendation to an end user. In some embodiments, EUD 503 may be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.

Remote server 504 is any computer system that serves at least some data and/or functionality to computer 501. Remote server 504 may be controlled and used by the same entity that operates computer 501. Remote server 504 represents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer 501. For example, in a hypothetical case where computer 501 is designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computer 501 from remote database 530 of remote server 504.

Public cloud 505 is any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloud 505 is performed by the computer hardware and/or software of cloud orchestration module 541. The computing resources provided by public cloud 505 are typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set 542, which is the universe of physical computers in and/or available to public cloud 505. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine set 543 and/or containers from container set 544. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration module 541 manages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gateway 540 is the collection of computer software, hardware, and firmware that allows public cloud 505 to communicate through WAN 502.

Private cloud 506 is similar to public cloud 505, except that the computing resources are only available for use by a single enterprise. While private cloud 506 is depicted as being in communication with WAN 502, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloud 505 and private cloud 506 are both part of a larger hybrid cloud.

While FIG. 5 depicts AI Governance Engine 110 in a first location, it should be appreciated that there are many embodiments of appropriate configurations for the AI governance engine 110. For example, the weights, biases, instructions, etc. may be housed in persistent storage in some embodiments, while the actual computational and inference mechanism may reside on or beside the processor.

The foregoing descriptions of the various embodiments of the present invention have been presented for purposes of illustration and example but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the invention. The terminology used herein was chosen to best explain the principles of the embodiment, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.