Patent Publication Number: US-2023142339-A1

Title: Recognition of user intents and associated entities using a neural network in an interaction environment

Description:
BACKGROUND 
     Interaction environments may include conversational artificial intelligence systems that receive a user input, such as a voice input, and then infer an intent in order to provide a response to the input. These systems are generally trained on large data sets, where each intent is trained to a specific entity, which creates a generally inflexible and unwieldy model. For example, systems may deploy a variety of different models that are specifically trained to each task and when small changes are instituted, the models are then retrained on newly annotated data. As a result, systems may be inflexible to new information or updates may be slow, which could limit the useability of the systems. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Various embodiments in accordance with the present disclosure will be described with reference to the drawings, in which: 
         FIG.  1    illustrates an example interaction environment, according to at least one embodiment; 
         FIG.  2    illustrates an example of a pipeline for intent and entity recognition, according to at least one embodiment; 
         FIG.  3 A  illustrates an example environment for intent recognition, according to at least one embodiment; 
         FIG.  3 B  illustrates an example environment for entity recognition, according to at least one embodiment; 
         FIG.  4    illustrates an example command definition for an interaction environment, according to at least one embodiment; 
         FIG.  5    illustrates an example process flow for intent and entity recognition, according to at least one embodiment; 
         FIG.  6 A  illustrates an example flow chart of a process for intent and entity recognition, according to at least one embodiment; 
         FIG.  6 B  illustrates an example flow chart of a process for intent and entity recognition, according to at least one embodiment; 
         FIG.  6 C  illustrates an example flow chart of a process for configuring an interaction environment, according to at least one embodiment; 
         FIG.  7    illustrates an example data center system, according to at least one embodiment; 
         FIG.  8    illustrates a computer system, according to at least one embodiment; 
         FIG.  9    illustrates a computer system, according to at least one embodiment; 
         FIG.  10    illustrates at least portions of a graphics processor, according to one or more embodiments; and 
         FIG.  11    illustrates at least portions of a graphics processor, according to one or more embodiments. 
     
    
    
     DETAILED DESCRIPTION 
     Approaches in accordance with various embodiments provide systems and methods for zero-shot approaches to interaction environments. In at least one embodiment, a zero-shot approach may be utilized for recognition of user intents, for example based a user input, such as an auditory input. Various embodiments may include one or more trained neural network models that receive an input, such as an auditory user query, and determine a label for the associated input that corresponds to an intent of the query. The label may be determined based, at least in part, on a probability the label corresponding to the intent exceeds a threshold. In at least one embodiment, a set of pre-determined labels may be provided, where user inputs are then evaluated against those labels to determine which label is most likely associated with the input. Various embodiments may further utilize one or more approaches, such as a zero-shot approach, to determine an entity associated with the intent of the input. For example, the entity may be determined, at least in part, by defining a question or phrase to describe the entity in a natural way. In various embodiments, an extractive question answer model may be utilized to answer the question or phrase in order to determine a value for a slot associated with an answer to the input. 
     Various embodiments of the present disclosure may enable one or more conversational artificial intelligence (AI) systems to recognize user commands (e.g., intents and entities) during natural language conversational interactions while providing flexibility for an operator to add new commands without extensive new training examples. Accordingly, embodiments may enable a user who wants to add voice or textual natural language commands to an application to do so without preparing, often manually, thousands of examples and training one or more neural network models for a specific use case. Furthermore, retraining steps may be reduced or eliminated using embodiments of the present disclosure. In at least one embodiment, systems and methods may also enable adding new commands to the system, in near real-time or in runtime. 
     An interaction environment  100  may be presented in a display area  102  that includes one or more content elements, as illustrated in  FIG.  1   . In at least one embodiment, interaction environment  100  may be associated with a conversational AI system that allows a user to interact with different content elements based, at least in part, on one or more inputs, such as a voice input, a textual input, selection of an area, or the like. The display area  102  may form a portion of an electronic device, such as a smart phone, personal computer, smart TV, a virtual realty system, an interaction kiosk, or the like. In this example, a display element  104  is illustrated that includes an object  106  corresponding to an automobile. The object  106  is illustrated in a rear-view where a bumper is visible. As will be described below, various embodiments enable a user to provide an input instruction, such as a voice instruction, to modify one or more aspects of the object  106  and/or to perform one or more supported actions within the interaction environment  100 . 
     The illustrated system further includes selectable content elements, which may include an input content element  108 , a save content element  110 , an exit content element  112 , and a property content element  114 . It should be appreciated that these selectable content elements are provided by way of example only and that other embodiments may include more or fewer content elements. Furthermore, different types of content elements may be utilized with different types of interaction properties, such as voice commands, manual inputs, or the like. Furthermore, the interaction environment may receive one or more scripts that include a sequence of actions that are used to initiate different commands associated with the selectable content elements. In operation, the user may interact with one or more of the content elements in order to perform one or more tasks or actions associated with the environment, such as changing properties of the object  106 . By way of example, the user may select the input content element  108 , such as by clicking on it (e.g., with a cursor controlled by a mouse or with a finger), by providing a verbal instruction, or the like. The user&#39;s command may then be received and one or more systems may determine the user&#39;s intent, determine an entity associated with the intent, and then perform one or more actions based, at least in part, on the user input. 
     Systems and methods may be directed toward generating a conversational AI using a zero-shot approach. Embodiments include a user-defined set of intents that are associated with a label. Each of these intents may have a corresponding question or follow on action, which may then be used to select a value to fill a slot. As an example, a user intent may relate to changing a car color, the corresponding question would be “which color” and the values to fill that slot (e.g., answer the question) could be any number of colors. During operation, a first trained network determines a probability that an input corresponds to a label, with the highest probability being selected, to determine an intent of the input. Then, a second trained network determines a follow on question for that intent to determine which value to populate a slot for the intent. The command may then be executed. The system enables development of a conversational AI with a reduced amount of training data and also provides a more natural way of coding the information because intents and questions may be provided in a natural way. 
     An architecture  200  may include one or more processing units, which may be locally hosted or part of one or more distributed systems, as shown in  FIG.  2   . In this example, an input  202  is provided at a local client  204 . As noted above, the local client  204  may be one or more electronic devices that are configured to receive a user input, such as a voice input, and is communicatively coupled to additional portions of the architecture  200 , either through on-system memory or via one or more network connections to one or more remote servers. The input may be a speech input, such as a user utterance that includes one or more phrases, which may be in the form of a question (e.g., query) or a command, among other options. In this example, the local client  204  may provide access to an interaction environment  206 . For example, the local client  204  may access, over a network, one or more computing units of a distributed computing environment that may provide access to the interaction environment  206 . In various embodiments, the interaction environment  206  may be accessible via one or more software programs stored on and/or executed by the local client  204 . By way of example, the local client  204  may include a kiosk positioned to assist individuals navigate an area or to answer questions or queries, the kiosk may include software instructions that are configured to provide users with access to the capabilities of the interaction environment  206 . 
     In operation, the user provides the input  202  to the local client  204 , which may further include one or more speech clients to enable processing of the input. By way of example, the speech client may perform one or more pre-processing steps, as well as evaluation of the speech, such as via automatic speech recognition, text-to-speech processing, natural language understanding, and the like. Moreover, it should be appreciated that one or more of these functions may be offloaded to a remote speech entity  208 , which may be hosted or otherwise part of a distributed computing environment accessible via one or more networks, or may be, at least in part, stored or executed on the local client  204 . The local client  204  may transmit the input to the speech entity  208  for processing, for example as an audio stream. The speech entity  208  may then determine queries, commands, questions, or the like from the audio stream using one or more processing modules. 
     In various embodiments, the speech entity  204  may further include one or more trained neural network models that enable recognition of an intent or the audio stream, or other input, associated with the input  202 . For example, the speech entity  204  may evaluate one or more portions of the audio stream to determine an intent of the audio stream, which may be based, at least in part, on an evaluation of whether the query corresponds to one or more intent labels. Various words or phrases from the audio stream may be evaluated and then a probability of the words or phrases corresponding to a label may be determined, where a highest probability label and/or a label exceeding a threshold and being a highest probability label, may be selected. In at least one embodiment, one or more additional train neural networks, such as an extractive question answer model, may, based at least in part on the intent, determine a follow on question associated with the query. The follow on question may relate to a query that is responsive to query where it is determined whether the follow on question logically follows the query, is contradictory to the query, or is neural. As an example, an input associated with the scene of  FIG.  1    may be “Change the color to blue” where the follow on query would ask “What color?” Thereafter, the system may evaluate a number of potential colors, which may correspond to values for an associated entity, in this instance color. The color “blue” may then be selected from the potential colors, if available, and then used to populate a slot, which provides an action for the system to follow, in this instance the action would be rendering the object in the color blue. 
     In at least one embodiment, the command to update or change the scene is transmitted to the interaction environment  206 , which may be a direct transmission from the speech entity  208  or from the local client  204 . The interaction environment  206  may then affect the change by performing the action and, in various embodiments, may provide a confirmation of the action, such as providing an auditory response indicating the action is complete. Additional interactions may then repeat the process with different intents, slots, entities, and values being identified, populated, and then having actions performed. 
     An intent classification system  300  may form at least a portion of speech entity  208 , as shown in  FIG.  3 A . It should be appreciated that the intent classification system  300  may include more or fewer components and that the current embodiment is shown for illustrative purposes only. In this example, an intent classification system  300  includes a classifier  302 , which may be a portion of a trained neural network. In various embodiments, the classifier  302  utilizes one or more zero-shot approaches (e.g., zero-shot learning) to predict classes associated with user inputs based, at least in part, on training data. As will be appreciated, classes used to train the system may be different from the classes (e.g., intents) utilized during operation of the system. In various embodiments, the intent classifier  302  receives, as an input, one or more words or word sequences, which may have undergone one or more preprocessing steps, and then determines a probability that the word or word sequence belongs to one or more intents classifications (e.g., labels). A highest probability score may then be selected for classifying the word or word sequences. Moreover, it should be appreciated that one or more thresholds may further be established for classification, where a probability that does not exceed a threshold, while still being a highest among a group, is not classified into the highest probability label. 
     In various embodiments, labels may be defined by one or more users or operators of the system, or may be predefined, and may be stored in a label data store  304 . Labels may be provided to the system by an entity operating or presenting the system to users, where the labels are selected, at least in part, on the interaction environment being presented. By way of example only, with interaction environment of  FIG.  1    is associated with an automobile, so labels may be associated with changing a color, changing a camera angle, and the like. However, these labels may be particularly selected for this particular interaction environment, as a label associated with an action such as “inserting a bush” or a “adding a wall” would not make sense or be related to the interaction environment. Accordingly, systems and methods may be utilized to establish specific labels for specific actions based, at least in part, on the interaction environment. As will be described, classifying certain actions within a label problems improved flexibility to the system, where specific training examples are not used for a specific environment, but rather, the zero-shot approach allows a trained system to then adapt to various different user-provided labels. 
     As noted above, the input provided by the user maybe processed via one or more processing systems  306 , which may include or be associated with one or more audio or textual processing systems, such as a natural language understanding (NLU) system  106  to enable humans to interact naturally with devices. The NLU system may be utilized to interpret context and intent of the input to generate a response. For example, the input may be preprocessed, which may include tokenization, lemmatization, stemming, and other processes. Additionally, the NLU system may include one or more deep learning models, such as a BERT model, to enable features such as entity recognition, intent recognition, sentiment analysis, and others. Moreover, various embodiments may further include automatic speech recognition (ASR), text-to-speech processing, and the like. One such example of these systems may be associated with one or more multimodal conversational AI services, such as Jarvis from NVIDIA Corporation. 
     A selection system  350  may form at least a portion of speech entity  208 , as shown in  FIG.  3 B . It should be appreciated that the selection system  350  may include more or fewer components and that the current embodiment is shown for illustrative purposes only. In this example, a selection system  350  includes an extractive question answer model  352 , which may be a trained neural network that is utilized to extract one or more portions of an input sequence to answer a natural language question associated with such a sequence. As noted above, for an input such as “paint the car blue” an intent may be determined as “related to car color” with the question being “what color?” In this example, the extractive question answer model  352  could then be utilized to answer the question of “what color,” which in this case would be “blue.” In various embodiments, the extractive question answer model may be a trained neural network system, such as Megatron from NVIDIA Corporation. 
     In various embodiments, a user or operator may populate a values data store  354  that includes a variety of different potential values for populating associated slots, which may be further related to different intents and/or questions associated with those intents. By way of example only, an intent may be related to changing a color, an associated slot may be a color, a question for that slot may be “which color?” and slot values may include different potential colors, such as white, red, black, blue, green, etc. Accordingly, a provider may enable predefined or predetermined configurations that may be rendered responsive to an input from the user. In at least one embodiment, a slot populator  356  determines which value, from the values data store  354  to populate the slot associated with the question, which leads to performance of one or more actions. Returning to the previous example, if the user had said “change the color to black,” the system would interpret intent to be related to changing a color, the slot associated with color, a question of “which color,” and then select from the slot values to identify black as a potential value and the populate an associated slot with “black.” Thereafter a value communicator  358  may proceed with transmitting information to the interaction environment to enable performance of the action associated with the input. 
     In at least one embodiment, command definitions  400  may be provided as an input to the interaction environment, as shown in  FIG.  4   . It should be appreciated that while the command definitions  400  are shown as a slot and intent table for the illustrated embodiment, various other types of data inputs and configurations may be provided with embodiments of the present disclosure. In this example, intents  402  are illustrated in a first column and their associated intent labels  404  are shown in a second column. As previous discussed, the intents may be related to one or more actions corresponding to an input provided by a user. By way of example, the intent may be associated with an action, such as opening a door, with an associated label such as “related to opening doors.” In at least one embodiment, a user may provide the command definitions  400  and may, in various embodiments, update the definitions in near-real time or at run time, which provides improved flexibility for the system. 
     As shown, intent labels  404  may be related to associated slots  406 , which may be populated with one or more values from the slot values  408 . In at least one embodiment, slot values  408  are determined based, at least in part, on their ability to answer a question with respect to the slot question  410 . That is, upon determining the intent, the extractive question answer model may then formulate a question, where an answer to the question is determined based, at least in part, on the input. Thereafter, a value may be selected from the slot value  408  and populated into the slot  406 . Accordingly, an associated response  412  may be provided to the user, along with a command to an interaction environment to proceed with executing the user&#39;s query. 
     A process flow  500  to extract an intent from a query, determine a responsive question, populate a slot with a value, and perform an action is illustrated in  FIG.  5   . In at least one embodiment, various software modules may be utilized to perform different steps of the illustrated flow, where one or more components may be hosted locally on a local client or may be accessible via one or more networks, such as at a remote server or as a portion of a distributed computing environment. In this example, an input  502  starts the flow, which corresponds to a user utterance of “Paint the car in blue color.” This utterance may be responsive to the user interacting with an environment showing an image or rendering of a car, such as the environment shown in  FIG.  1   . The input may be received by one or more local clients, for example via a microphone, and may be further processed either on the local client or using one or more remote systems. 
     Various embodiments extract an intent  504  from the input  502 . In this example, intent may be determined by evaluating one or more portions of the utterance, such as word or word phrases, via one or more trained machine learning systems. For example, the utterance may be evaluated and one or more keywords or phrases may be extracted, which may be utilized to determine an intent. The intent may be associated with a predetermined or pre-loaded intent, such as one provided by a provider of the system, where the intents may correspond to one or more capabilities of the systems. The intent  504  may be determined by classifying the utterance based, at least in part, on a probability the intent is associated with one or more labels. In this example, certain phrases are utilized to determine the intent, such as “paint,” “blue,” and “color,” to provide a high probability that the input  502  is associated with a label corresponding to “color change.” Accordingly, follow on actions according to the determined label may be performed, as further illustrated. 
     The determined intent may be processed by an extractive question answer model  506 . For example, the model  506  may process a question, in natural language, responsive to the intent. In this example, the question is “What color?” and the answer may be extracted from the initial input, which is “blue,” as shown in the slot values step  508 . The answer may then be compared to one or more values, such as from the values data store  354 . If there is a match, then the value may be utilized for slot filling  510 . For example, the “slot” may correspond to a value that within a command to perform one or more actions  512 , which in this example, is to render the car in a blue color. Subsequent inputs may be further processed to determine intents, associated questions, and slots. In at least one embodiment, additional tools may be provided where an intent is not determinable, such as a help function that requests additional information. 
       FIG.  6 A  illustrates an example process  600  for determining a user intent to execute an action within an interaction environment. It should be understood that for this and other processes presented herein that there can be additional, fewer, or alternative steps performed in similar or alternative order, or at least partially in parallel, within the scope of various embodiments unless otherwise specifically stated. In this example, an input is received at an interaction environment  602 . The input may be a voice input, such as an utterance provided by a user. It should be appreciated that inputs may also include an audio recording, an audio segment extracted from a video, a textual input, or the like. An intent may be determined from the input  604 . In at least one embodiment, the intent is evaluated using a zero-shot approach and a probability for an intent is determined. The probability may be evaluated against a list of pre-determined intent labels, which the labels are provided by a provider associated with the interaction environment. 
     In various embodiments, an entity associated with the intent is determined  606 . The entity may correspond to a slot within a table that may be populated in order to determine a response to the input. In at least one embodiment, the entity is determined based, at least in part, on an extractive question answer model where a question is proposed responsive to the user input and an answer to satisfy the slot is determined The entity have a list of potential associated values, where a value is selected based, at least in part, on the input  608 . The selected value may be used to populate the entity  610  such that a task can be executed responsive to the input  612 . 
       FIG.  6 B  illustrates an example process  620  for determining a user intent and associated value to perform an action. In this example, a user query is received  622 . As noted, the user query may be an auditory input, among other options. A first trained neural network may be used to determine an intent of the user query  624 . In at least one embodiment, the trained neural network utilizes a zero-shot approach where one or more features of the query are evaluated to determine a probability that the intent is related to one or more pre-defined intent labels. In at least one embodiment, a second trained neural network may determine an entity associated with the label  626  and a value for that entity  628 . The second trained neural network may utilize an extractive question and answer model to determine the appropriate entity, for example by formulating a question associated with the input, and then determining whether a value is supported from a list of pre-determined values. The value may be utilized to populate the entity so that a command may be transmitted in order to perform one or more actions associated with the user query  630 . 
       FIG.  6 C  illustrates an example process  650  for configuring an interaction environment. In this example, a command definition for an interaction environment is received  652 . The command definition may include a set of intents and associated labels for the intents. Moreover, in embodiments, each label may include a corresponding slot to be populated with one or more values from a list of corresponding values. The interaction environment may be configured based, at least in part, on the command definition  654 . In at least one embodiment, the interaction environment is configured without training one or more machine learning systems with information associated with the command definition. That is, an existing trained model may be utilized that is not specially trained using the command definition. One or more updates to the command definition may be provided  656 . Updates may include additional intents or labels, additional values, or the like. The interaction environment may be updated using the one or more updates  658 . In at least one embodiment, the update is further done without updating or modifying the one or more machine learning systems associated with the interaction environment. 
     Data Center 
       FIG.  7    illustrates an example data center  700 , in which at least one embodiment may be used. In at least one embodiment, data center  700  includes a data center infrastructure layer  710 , a framework layer  720 , a software layer  730 , and an application layer  740 . 
     In at least one embodiment, as shown in  FIG.  7   , data center infrastructure layer  710  may include a resource orchestrator  712 , grouped computing resources  714 , and node computing resources (“node C.R.s”)  716 ( 1 )- 716 (N), where “N” represents any whole, positive integer. In at least one embodiment, node C.R.s  716 ( 1 )- 716 (N) may include, but are not limited to, any number of central processing units (“CPUs”) or other processors (including accelerators, field programmable gate arrays (FPGAs), graphics processors, etc.), memory devices (e.g., dynamic read-only memory), storage devices (e.g., solid state or disk drives), network input/output (“NW I/O”) devices, network switches, virtual machines (“VMs”), power modules, and cooling modules, etc. In at least one embodiment, one or more node C.R.s from among node C.R.s  716 ( 1 )- 716 (N) may be a server having one or more of above-mentioned computing resources. 
     In at least one embodiment, grouped computing resources  714  may include separate groupings of node C.R.s housed within one or more racks (not shown), or many racks housed in data centers at various geographical locations (also not shown). Separate groupings of node C.R.s within grouped computing resources  714  may include grouped compute, network, memory or storage resources that may be configured or allocated to support one or more workloads. In at least one embodiment, several node C.R.s including CPUs or processors may grouped within one or more racks to provide compute resources to support one or more workloads. In at least one embodiment, one or more racks may also include any number of power modules, cooling modules, and network switches, in any combination. 
     In at least one embodiment, resource orchestrator  712  may configure or otherwise control one or more node C.R.s  716 ( 1 )- 716 (N) and/or grouped computing resources  714 . In at least one embodiment, resource orchestrator  712  may include a software design infrastructure (“SDI”) management entity for data center  700 . In at least one embodiment, resource orchestrator may include hardware, software or some combination thereof. 
     In at least one embodiment, as shown in  FIG.  7   , framework layer  720  includes a job scheduler  722 , a configuration manager  724 , a resource manager  726  and a distributed file system  728 . In at least one embodiment, framework layer  720  may include a framework to support software  732  of software layer  730  and/or one or more application(s)  742  of application layer  740 . In at least one embodiment, software  732  or application(s)  742  may respectively include web-based service software or applications, such as those provided by Amazon Web Services, Google Cloud and Microsoft Azure. In at least one embodiment, framework layer  720  may be, but is not limited to, a type of free and open-source software web application framework such as Apache Spark™ (hereinafter “Spark”) that may utilize distributed file system  728  for large-scale data processing (e.g., “big data”). In at least one embodiment, job scheduler  722  may include a Spark driver to facilitate scheduling of workloads supported by various layers of data center  700 . In at least one embodiment, configuration manager  724  may be capable of configuring different layers such as software layer  730  and framework layer  720  including Spark and distributed file system  728  for supporting large-scale data processing. In at least one embodiment, resource manager  726  may be capable of managing clustered or grouped computing resources mapped to or allocated for support of distributed file system  728  and job scheduler  722 . In at least one embodiment, clustered or grouped computing resources may include grouped computing resource  714  at data center infrastructure layer  710 . In at least one embodiment, resource manager  726  may coordinate with resource orchestrator  712  to manage these mapped or allocated computing resources. 
     In at least one embodiment, software  732  included in software layer  730  may include software used by at least portions of node C.R.s  716 ( 1 )- 716 (N), grouped computing resources  714 , and/or distributed file system  728  of framework layer  720 . The one or more types of software may include, but are not limited to, Internet web page search software, e-mail virus scan software, database software, and streaming video content software. 
     In at least one embodiment, application(s)  742  included in application layer  740  may include one or more types of applications used by at least portions of node C.R.s  716 ( 1 )- 716 (N), grouped computing resources  714 , and/or distributed file system  728  of framework layer  720 . One or more types of applications may include, but are not limited to, any number of a genomics application, a cognitive compute, and a machine learning application, including training or inferencing software, machine learning framework software (e.g., PyTorch, TensorFlow, Caffe, etc.) or other machine learning applications used in conjunction with one or more embodiments. 
     In at least one embodiment, any of configuration manager  724 , resource manager  726 , and resource orchestrator  712  may implement any number and type of self-modifying actions based on any amount and type of data acquired in any technically feasible fashion. In at least one embodiment, self-modifying actions may relieve a data center operator of data center  700  from making possibly bad configuration decisions and possibly avoiding underutilized and/or poor performing portions of a data center. 
     In at least one embodiment, data center  700  may include tools, services, software or other resources to train one or more machine learning models or predict or infer information using one or more machine learning models according to one or more embodiments described herein. For example, in at least one embodiment, a machine learning model may be trained by calculating weight parameters according to a neural network architecture using software and computing resources described above with respect to data center  700 . In at least one embodiment, trained machine learning models corresponding to one or more neural networks may be used to infer or predict information using resources described above with respect to data center  700  by using weight parameters calculated through one or more training techniques described herein. 
     In at least one embodiment, data center may use CPUs, application-specific integrated circuits (ASICs), GPUs, FPGAs, or other hardware to perform training and/or inferencing using above-described resources. Moreover, one or more software and/or hardware resources described above may be configured as a service to allow users to train or performing inferencing of information, such as image recognition, speech recognition, or other artificial intelligence services. 
     Such components can be used for executing commands in interaction environments. 
     Computer Systems 
       FIG.  8    is a block diagram illustrating an exemplary computer system, which may be a system with interconnected devices and components, a system-on-a-chip (SOC) or some combination thereof  800  formed with a processor that may include execution units to execute an instruction, according to at least one embodiment. In at least one embodiment, computer system  800  may include, without limitation, a component, such as a processor  802  to employ execution units including logic to perform algorithms for process data, in accordance with present disclosure, such as in embodiment described herein. In at least one embodiment, computer system  800  may include processors, such as PENTIUM® Processor family, Xeon™, Itanium®, XScale™ and/or StrongARM™, Intel® Core™, or Intel® Nervana™ microprocessors available from Intel Corporation of Santa Clara, Calif., although other systems (including PCs having other microprocessors, engineering workstations, set-top boxes and like) may also be used. In at least one embodiment, computer system  800  may execute a version of WINDOWS&#39; operating system available from Microsoft Corporation of Redmond, Wash., although other operating systems (UNIX and Linux for example), embedded software, and/or graphical user interfaces, may also be used. 
     Embodiments may be used in other devices such as handheld devices and embedded applications. Some examples of handheld devices include cellular phones, Internet Protocol devices, digital cameras, personal digital assistants (“PDAs”), and handheld PCs. In at least one embodiment, embedded applications may include a microcontroller, a digital signal processor (“DSP”), system on a chip, network computers (“NetPCs”), edge computing devices, set-top boxes, network hubs, wide area network (“WAN”) switches, or any other system that may perform one or more instructions in accordance with at least one embodiment. 
     In at least one embodiment, computer system  800  may include, without limitation, processor  802  that may include, without limitation, one or more execution units  808  to perform machine learning model training and/or inferencing according to techniques described herein. In at least one embodiment, computer system  800  is a single processor desktop or server system, but in another embodiment computer system  800  may be a multiprocessor system. In at least one embodiment, processor  802  may include, without limitation, a complex instruction set computer (“CISC”) microprocessor, a reduced instruction set computing (“RISC”) microprocessor, a very long instruction word (“VLIW”) microprocessor, a processor implementing a combination of instruction sets, or any other processor device, such as a digital signal processor, for example. In at least one embodiment, processor  802  may be coupled to a processor bus  810  that may transmit data signals between processor  802  and other components in computer system  800 . 
     In at least one embodiment, processor  802  may include, without limitation, a Level 1 (“L1”) internal cache memory (“cache”)  804 . In at least one embodiment, processor  802  may have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory may reside external to processor  802 . Other embodiments may also include a combination of both internal and external caches depending on particular implementation and needs. In at least one embodiment, register file  806  may store different types of data in various registers including, without limitation, integer registers, floating point registers, status registers, and instruction pointer register. 
     In at least one embodiment, execution unit  808 , including, without limitation, logic to perform integer and floating point operations, also resides in processor  802 . In at least one embodiment, processor  802  may also include a microcode (“ucode”) read only memory (“ROM”) that stores microcode for certain macro instructions. In at least one embodiment, execution unit  808  may include logic to handle a packed instruction set  809 . In at least one embodiment, by including packed instruction set  809  in an instruction set of a general-purpose processor  802 , along with associated circuitry to execute instructions, operations used by many multimedia applications may be performed using packed data in a general-purpose processor  802 . In one or more embodiments, many multimedia applications may be accelerated and executed more efficiently by using full width of a processor&#39;s data bus for performing operations on packed data, which may eliminate need to transfer smaller units of data across processor&#39;s data bus to perform one or more operations one data element at a time. 
     In at least one embodiment, execution unit  808  may also be used in microcontrollers, embedded processors, graphics devices, DSPs, and other types of logic circuits. In at least one embodiment, computer system  800  may include, without limitation, a memory  820 . In at least one embodiment, memory  820  may be implemented as a Dynamic Random Access Memory (“DRAM”) device, a Static Random Access Memory (“SRAM”) device, flash memory device, or other memory device. In at least one embodiment, memory  820  may store instruction(s)  819  and/or data  821  represented by data signals that may be executed by processor  802 . 
     In at least one embodiment, system logic chip may be coupled to processor bus  810  and memory  820 . In at least one embodiment, system logic chip may include, without limitation, a memory controller hub (“MCH”)  816 , and processor  802  may communicate with MCH  816  via processor bus  810 . In at least one embodiment, MCH  816  may provide a high bandwidth memory path  818  to memory  820  for instruction and data storage and for storage of graphics commands, data and textures. In at least one embodiment, MCH  816  may direct data signals between processor  802 , memory  820 , and other components in computer system  800  and to bridge data signals between processor bus  810 , memory  820 , and a system I/O  822 . In at least one embodiment, system logic chip may provide a graphics port for coupling to a graphics controller. In at least one embodiment, MCH  816  may be coupled to memory  820  through a high bandwidth memory path  818  and graphics/video card  812  may be coupled to MCH  816  through an Accelerated Graphics Port (“AGP”) interconnect  814 . 
     In at least one embodiment, computer system  800  may use system I/O  822  that is a proprietary hub interface bus to couple MCH  816  to I/O controller hub (“ICH”)  830 . In at least one embodiment, ICH  830  may provide direct connections to some I/O devices via a local I/O bus. In at least one embodiment, local I/O bus may include, without limitation, a high-speed I/O bus for connecting peripherals to memory  820 , chipset, and processor  802 . Examples may include, without limitation, an audio controller  829 , a firmware hub (“flash BIOS”)  828 , a wireless transceiver  826 , a data storage  824 , a legacy I/O controller  823  containing user input and keyboard interfaces  825 , a serial expansion port  827 , such as Universal Serial Bus (“USB”), and a network controller  834 . Data storage  824  may comprise a hard disk drive, a floppy disk drive, a CD-ROM device, a flash memory device, or other mass storage device. 
     In at least one embodiment,  FIG.  8    illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,  FIG.  8    may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof In at least one embodiment, one or more components of computer system  800  are interconnected using compute express link (CXL) interconnects. 
     Such components can be used for executing commands in interaction environments. 
       FIG.  9    is a block diagram illustrating an electronic device  900  for utilizing a processor  910 , according to at least one embodiment. In at least one embodiment, electronic device  900  may be, for example and without limitation, a notebook, a tower server, a rack server, a blade server, a laptop, a desktop, a tablet, a mobile device, a phone, an embedded computer, or any other suitable electronic device. 
     In at least one embodiment, system  900  may include, without limitation, processor  910  communicatively coupled to any suitable number or kind of components, peripherals, modules, or devices. In at least one embodiment, processor  910  coupled using a bus or interface, such as a 1° C. bus, a System Management Bus (“SMBus”), a Low Pin Count (LPC) bus, a Serial Peripheral Interface (“SPI”), a High Definition Audio (“HDA”) bus, a Serial Advance Technology Attachment (“SATA”) bus, a Universal Serial Bus (“USB”) (versions 1, 2, 3), or a Universal Asynchronous Receiver/Transmitter (“UART”) bus. In at least one embodiment,  FIG.  9    illustrates a system, which includes interconnected hardware devices or “chips”, whereas in other embodiments,  FIG.  9    may illustrate an exemplary System on a Chip (“SoC”). In at least one embodiment, devices illustrated in  FIG.  9    may be interconnected with proprietary interconnects, standardized interconnects (e.g., PCIe) or some combination thereof. In at least one embodiment, one or more components of  FIG.  9    are interconnected using compute express link (CXL) interconnects. 
     In at least one embodiment,  FIG.  9    may include a display  924 , a touch screen  925 , a touch pad  930 , a Near Field Communications unit (“NFC”)  945 , a sensor hub  940 , a thermal sensor  946 , an Express Chipset (“EC”)  935 , a Trusted Platform Module (“TPM”)  938 , BIOS/firmware/flash memory (“BIOS, FW Flash”)  922 , a DSP  960 , a drive  920  such as a Solid State Disk (“SSD”) or a Hard Disk Drive (“HDD”), a wireless local area network unit (“WLAN”)  950 , a Bluetooth unit  952 , a Wireless Wide Area Network unit (“WWAN”)  956 , a Global Positioning System (GPS)  955 , a camera (“USB 3.0 camera”)  954  such as a USB 3.0 camera, and/or a Low Power Double Data Rate (“LPDDR”) memory unit (“LPDDR3”)  915  implemented in, for example, LPDDR3 standard. These components may each be implemented in any suitable manner. 
     In at least one embodiment, other components may be communicatively coupled to processor  910  through components discussed above. In at least one embodiment, an accelerometer  941 , Ambient Light Sensor (“ALS”)  942 , compass  943 , and a gyroscope  944  may be communicatively coupled to sensor hub  940 . In at least one embodiment, thermal sensor  939 , a fan  937 , a keyboard  946 , and a touch pad  930  may be communicatively coupled to EC  935 . In at least one embodiment, speaker  963 , headphones  964 , and microphone (“mic”)  965  may be communicatively coupled to an audio unit (“audio codec and class d amp”)  962 , which may in turn be communicatively coupled to DSP  960 . In at least one embodiment, audio unit  964  may include, for example and without limitation, an audio coder/decoder (“codec”) and a class D amplifier. In at least one embodiment, SIM card (“SIM”)  957  may be communicatively coupled to WWAN unit  956 . In at least one embodiment, components such as WLAN unit  950  and Bluetooth unit  952 , as well as WWAN unit  956  may be implemented in a Next Generation Form Factor (“NGFF”). 
     Such components can be used for executing commands in interaction environments. 
       FIG.  10    is a block diagram of a processing system, according to at least one embodiment. In at least one embodiment, system  1000  includes one or more processors  1002  and one or more graphics processors  1008 , and may be a single processor desktop system, a multiprocessor workstation system, or a server system or datacenter having a large number of collectively or separably managed processors  1002  or processor cores  1007 . In at least one embodiment, system  1000  is a processing platform incorporated within a system-on-a-chip (SoC) integrated circuit for use in mobile, handheld, or embedded devices. 
     In at least one embodiment, system  1000  can include, or be incorporated within a server-based gaming platform, a cloud computing host platform, a virtualized computing platform, a game console, including a game and media console, a mobile gaming console, a handheld game console, or an online game console. In at least one embodiment, system  1000  is a mobile phone, smart phone, tablet computing device or mobile Internet device. In at least one embodiment, processing system  1000  can also include, couple with, or be integrated within a wearable device, such as a smart watch wearable device, smart eyewear device, augmented reality device, edge device, Internet of Things (“IoT”) device, or virtual reality device. In at least one embodiment, processing system  1000  is a television or set top box device having one or more processors  1002  and a graphical interface generated by one or more graphics processors  1008 . 
     In at least one embodiment, one or more processors  1002  each include one or more processor cores  1007  to process instructions which, when executed, perform operations for system and user software. In at least one embodiment, each of one or more processor cores  1007  is configured to process a specific instruction set  1009 . In at least one embodiment, instruction set  1009  may facilitate Complex Instruction Set Computing (CISC), Reduced Instruction Set Computing (RISC), or computing via a Very Long Instruction Word (VLIW). In at least one embodiment, processor cores  1007  may each process a different instruction set  1009 , which may include instructions to facilitate emulation of other instruction sets. In at least one embodiment, processor core  1007  may also include other processing devices, such a Digital Signal Processor (DSP). 
     In at least one embodiment, processor  1002  includes cache memory  1004 . In at least one embodiment, processor  1002  can have a single internal cache or multiple levels of internal cache. In at least one embodiment, cache memory is shared among various components of processor  1002 . In at least one embodiment, processor  1002  also uses an external cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC)) (not shown), which may be shared among processor cores  1007  using known cache coherency techniques. In at least one embodiment, register file  1006  is additionally included in processor  1002  which may include different types of registers for storing different types of data (e.g., integer registers, floating point registers, status registers, and an instruction pointer register). In at least one embodiment, register file  1006  may include general-purpose registers or other registers. 
     In at least one embodiment, one or more processor(s)  1002  are coupled with one or more interface bus(es)  1010  to transmit communication signals such as address, data, or control signals between processor  1002  and other components in system  1000 . In at least one embodiment, interface bus  1010 , in one embodiment, can be a processor bus, such as a version of a Direct Media Interface (DMI) bus. In at least one embodiment, interface  1010  is not limited to a DMI bus, and may include one or more Peripheral Component Interconnect buses (e.g., PCI, PCI Express), memory busses, or other types of interface busses. In at least one embodiment processor(s)  1002  include an integrated memory controller  1016  and a platform controller hub  1030 . In at least one embodiment, memory controller  1016  facilitates communication between a memory device and other components of system  1000 , while platform controller hub (PCH)  1030  provides connections to I/O devices via a local I/O bus. 
     In at least one embodiment, memory device  1020  can be a dynamic random access memory (DRAM) device, a static random access memory (SRAM) device, flash memory device, phase-change memory device, or some other memory device having suitable performance to serve as process memory. In at least one embodiment memory device  1020  can operate as system memory for system  1000 , to store data  1022  and instructions  1021  for use when one or more processors  1002  executes an application or process. In at least one embodiment, memory controller  1016  also couples with an optional external graphics processor  1012 , which may communicate with one or more graphics processors  1008  in processors  1002  to perform graphics and media operations. In at least one embodiment, a display device  1011  can connect to processor(s)  1002 . In at least one embodiment display device  1011  can include one or more of an internal display device, as in a mobile electronic device or a laptop device or an external display device attached via a display interface (e.g., DisplayPort, etc.). In at least one embodiment, display device  1011  can include a head mounted display (HMD) such as a stereoscopic display device for use in virtual reality (VR) applications or augmented reality (AR) applications. 
     In at least one embodiment, platform controller hub  1030  enables peripherals to connect to memory device  1020  and processor  1002  via a high-speed I/O bus. In at least one embodiment, I/O peripherals include, but are not limited to, an audio controller  1046 , a network controller  1034 , a firmware interface  1028 , a wireless transceiver  1026 , touch sensors  1025 , a data storage device  1024  (e.g., hard disk drive, flash memory, etc.). In at least one embodiment, data storage device  1024  can connect via a storage interface (e.g., SATA) or via a peripheral bus, such as a Peripheral Component Interconnect bus (e.g., PCI, PCI Express). In at least one embodiment, touch sensors  1025  can include touch screen sensors, pressure sensors, or fingerprint sensors. In at least one embodiment, wireless transceiver  1026  can be a Wi-Fi transceiver, a Bluetooth transceiver, or a mobile network transceiver such as a 3G, 4G, or Long Term Evolution (LTE) transceiver. In at least one embodiment, firmware interface  1028  enables communication with system firmware, and can be, for example, a unified extensible firmware interface (UEFI). In at least one embodiment, network controller  1034  can enable a network connection to a wired network. In at least one embodiment, a high-performance network controller (not shown) couples with interface bus  1010 . In at least one embodiment, audio controller  1046  is a multi-channel high definition audio controller. In at least one embodiment, system  1000  includes an optional legacy I/O controller  1040  for coupling legacy (e.g., Personal System 2 (PS/2)) devices to system. In at least one embodiment, platform controller hub  1030  can also connect to one or more Universal Serial Bus (USB) controllers  1042  connect input devices, such as keyboard and mouse  1043  combinations, a camera  1044 , or other USB input devices. 
     In at least one embodiment, an instance of memory controller  1016  and platform controller hub  1030  may be integrated into a discreet external graphics processor, such as external graphics processor  1012 . In at least one embodiment, platform controller hub  1030  and/or memory controller  1016  may be external to one or more processor(s)  1002 . For example, in at least one embodiment, system  1000  can include an external memory controller  1016  and platform controller hub  1030 , which may be configured as a memory controller hub and peripheral controller hub within a system chipset that is in communication with processor(s)  1002 . 
     Such components can be used for executing commands in interaction environments. 
       FIG.  11    is a block diagram of a processor  1100  having one or more processor cores  1102 A- 1102 N, an integrated memory controller  1114 , and an integrated graphics processor  1108 , according to at least one embodiment. In at least one embodiment, processor  1100  can include additional cores up to and including additional core  1102 N represented by dashed lined boxes. In at least one embodiment, each of processor cores  1102 A- 1102 N includes one or more internal cache units  1104 A- 1104 N. In at least one embodiment, each processor core also has access to one or more shared cached units  1106 . 
     In at least one embodiment, internal cache units  1104 A- 1104 N and shared cache units  1106  represent a cache memory hierarchy within processor  1100 . In at least one embodiment, cache memory units  1104 A- 1104 N may include at least one level of instruction and data cache within each processor core and one or more levels of shared mid-level cache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or other levels of cache, where a highest level of cache before external memory is classified as an LLC. In at least one embodiment, cache coherency logic maintains coherency between various cache units  1106  and  1104 A- 1104 N. 
     In at least one embodiment, processor  1100  may also include a set of one or more bus controller units  1116  and a system agent core  1110 . In at least one embodiment, one or more bus controller units  1116  manage a set of peripheral buses, such as one or more PCI or PCI express busses. In at least one embodiment, system agent core  1110  provides management functionality for various processor components. In at least one embodiment, system agent core  1110  includes one or more integrated memory controllers  1114  to manage access to various external memory devices (not shown). 
     In at least one embodiment, one or more of processor cores  1102 A- 1102 N include support for simultaneous multi-threading. In at least one embodiment, system agent core  1110  includes components for coordinating and operating cores  1102 A- 1102 N during multi-threaded processing. In at least one embodiment, system agent core  1110  may additionally include a power control unit (PCU), which includes logic and components to regulate one or more power states of processor cores  1102 A- 1102 N and graphics processor  1108 . 
     In at least one embodiment, processor  1100  additionally includes graphics processor  1108  to execute graphics processing operations. In at least one embodiment, graphics processor  1108  couples with shared cache units  1106 , and system agent core  1110 , including one or more integrated memory controllers  1114 . In at least one embodiment, system agent core  1110  also includes a display controller  1111  to drive graphics processor output to one or more coupled displays. In at least one embodiment, display controller  1111  may also be a separate module coupled with graphics processor  1108  via at least one interconnect, or may be integrated within graphics processor  1108 . 
     In at least one embodiment, a ring based interconnect unit  1112  is used to couple internal components of processor  1100 . In at least one embodiment, an alternative interconnect unit may be used, such as a point-to-point interconnect, a switched interconnect, or other techniques. In at least one embodiment, graphics processor  1108  couples with ring interconnect  1112  via an I/O link  1113 . 
     In at least one embodiment, I/O link  1113  represents at least one of multiple varieties of I/O interconnects, including an on package I/O interconnect which facilitates communication between various processor components and a high-performance embedded memory module  1118 , such as an eDRAM module. In at least one embodiment, each of processor cores  1102 A- 1102 N and graphics processor  1108  use embedded memory modules  1118  as a shared Last Level Cache. 
     In at least one embodiment, processor cores  1102 A- 1102 N are homogenous cores executing a common instruction set architecture. In at least one embodiment, processor cores  1102 A- 1102 N are heterogeneous in terms of instruction set architecture (ISA), where one or more of processor cores  1102 A- 1102 N execute a common instruction set, while one or more other cores of processor cores  1102 A- 1102 N executes a subset of a common instruction set or a different instruction set. In at least one embodiment, processor cores  1102 A- 1102 N are heterogeneous in terms of microarchitecture, where one or more cores having a relatively higher power consumption couple with one or more power cores having a lower power consumption. In at least one embodiment, processor  1100  can be implemented on one or more chips or as an SoC integrated circuit. 
     Such components can be used for executing commands in interaction environments. 
     Other variations are within spirit of present disclosure. Thus, while disclosed techniques are susceptible to various modifications and alternative constructions, certain illustrated embodiments thereof are shown in drawings and have been described above in detail. It should be understood, however, that there is no intention to limit disclosure to specific form or forms disclosed, but on contrary, intention is to cover all modifications, alternative constructions, and equivalents falling within spirit and scope of disclosure, as defined in appended claims. 
     Use of terms “a” and “an” and “the” and similar referents in context of describing disclosed embodiments (especially in context of following claims) are to be construed to cover both singular and plural, unless otherwise indicated herein or clearly contradicted by context, and not as a definition of a term. Terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (meaning “including, but not limited to,”) unless otherwise noted. Term “connected,” when unmodified and referring to physical connections, is to be construed as partly or wholly contained within, attached to, or joined together, even if there is something intervening. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within range, unless otherwise indicated herein and each separate value is incorporated into specification as if it were individually recited herein. Use of term “set” (e.g., “a set of items”) or “subset,” unless otherwise noted or contradicted by context, is to be construed as a nonempty collection comprising one or more members. Further, unless otherwise noted or contradicted by context, term “subset” of a corresponding set does not necessarily denote a proper subset of corresponding set, but subset and corresponding set may be equal. 
     Conjunctive language, such as phrases of form “at least one of A, B, and C,” or “at least one of A, B and C,” unless specifically stated otherwise or otherwise clearly contradicted by context, is otherwise understood with context as used in general to present that an item, term, etc., may be either A or B or C, or any nonempty subset of set of A and B and C. For instance, in illustrative example of a set having three members, conjunctive phrases “at least one of A, B, and C” and “at least one of A, B and C” refer to any of following sets: {A}, {B}, {C}, {A, B}, {A, C}, {B, C}, {A, B, C}. Thus, such conjunctive language is not generally intended to imply that certain embodiments require at least one of A, at least one of B, and at least one of C each to be present. In addition, unless otherwise noted or contradicted by context, term “plurality” indicates a state of being plural (e.g., “a plurality of items” indicates multiple items). A plurality is at least two items, but can be more when so indicated either explicitly or by context. Further, unless stated otherwise or otherwise clear from context, phrase “based on” means “based at least in part on” and not “based solely on.” 
     Operations of processes described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. In at least one embodiment, a process such as those processes described herein (or variations and/or combinations thereof) is performed under control of one or more computer systems configured with executable instructions and is implemented as code (e.g., executable instructions, one or more computer programs or one or more applications) executing collectively on one or more processors, by hardware or combinations thereof. In at least one embodiment, code is stored on a computer-readable storage medium, for example, in form of a computer program comprising a plurality of instructions executable by one or more processors. In at least one embodiment, a computer-readable storage medium is a non-transitory computer-readable storage medium that excludes transitory signals (e.g., a propagating transient electric or electromagnetic transmission) but includes non-transitory data storage circuitry (e.g., buffers, cache, and queues) within transceivers of transitory signals. In at least one embodiment, code (e.g., executable code or source code) is stored on a set of one or more non-transitory computer-readable storage media having stored thereon executable instructions (or other memory to store executable instructions) that, when executed (i.e., as a result of being executed) by one or more processors of a computer system, cause computer system to perform operations described herein. A set of non-transitory computer-readable storage media, in at least one embodiment, comprises multiple non-transitory computer-readable storage media and one or more of individual non-transitory storage media of multiple non-transitory computer-readable storage media lack all of code while multiple non-transitory computer-readable storage media collectively store all of code. In at least one embodiment, executable instructions are executed such that different instructions are executed by different processors—for example, a non-transitory computer-readable storage medium store instructions and a main central processing unit (“CPU”) executes some of instructions while a graphics processing unit (“GPU”) and/or a data processing unit (“DPU”) executes other instructions. In at least one embodiment, different components of a computer system have separate processors and different processors execute different subsets of instructions. 
     Accordingly, in at least one embodiment, computer systems are configured to implement one or more services that singly or collectively perform operations of processes described herein and such computer systems are configured with applicable hardware and/or software that enable performance of operations. Further, a computer system that implements at least one embodiment of present disclosure is a single device and, in another embodiment, is a distributed computer system comprising multiple devices that operate differently such that distributed computer system performs operations described herein and such that a single device does not perform all operations. 
     Use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate embodiments of disclosure and does not pose a limitation on scope of disclosure unless otherwise claimed. No language in specification should be construed as indicating any non-claimed element as essential to practice of disclosure. 
     All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein. 
     In description and claims, terms “coupled” and “connected,” along with their derivatives, may be used. It should be understood that these terms may be not intended as synonyms for each other. Rather, in particular examples, “connected” or “coupled” may be used to indicate that two or more elements are in direct or indirect physical or electrical contact with each other. “Coupled” may also mean that two or more elements are not in direct contact with each other, but yet still co-operate or interact with each other. 
     Unless specifically stated otherwise, it may be appreciated that throughout specification terms such as “processing,” “computing,” “calculating,” “determining,” or like, refer to action and/or processes of a computer or computing system, or similar electronic computing device, that manipulate and/or transform data represented as physical, such as electronic, quantities within computing system&#39;s registers and/or memories into other data similarly represented as physical quantities within computing system&#39;s memories, registers or other such information storage, transmission or display devices. 
     In a similar manner, term “processor” may refer to any device or portion of a device that processes electronic data from registers and/or memory and transform that electronic data into other electronic data that may be stored in registers and/or memory. As non-limiting examples, “processor” may be any processor capable of general purpose processing such as a CPU, GPU, or DPU. As non-limiting examples, “processor” may be any microcontroller or dedicated processing unit such as a DSP, image signal processor (“ISP”), arithmetic logic unit (“ALU”), vision processing unit (“VPU”), tree traversal unit (“TTU”), ray tracing core, tensor tracing core, tensor processing unit (“TPU”), embedded control unit (“ECU”), and the like. As non-limiting examples, “processor” may be a hardware accelerator, such as a PVA (programmable vision accelerator), DLA (deep learning accelerator), etc. As non-limiting examples, “processor” may also include one or more virtual instances of a CPU, GPU, etc., hosted on an underlying hardware component executing one or more virtual machines. A “computing platform” may comprise one or more processors. As used herein, “software” processes may include, for example, software and/or hardware entities that perform work over time, such as tasks, threads, and intelligent agents. Also, each process may refer to multiple processes, for carrying out instructions in sequence or in parallel, continuously or intermittently. Terms “system” and “method” are used herein interchangeably insofar as system may embody one or more methods and methods may be considered a system. 
     In present document, references may be made to obtaining, acquiring, receiving, or inputting analog or digital data into a subsystem, computer system, or computer-implemented machine. Obtaining, acquiring, receiving, or inputting analog and digital data can be accomplished in a variety of ways such as by receiving data as a parameter of a function call or a call to an application programming interface. In some implementations, process of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a serial or parallel interface. In another implementation, process of obtaining, acquiring, receiving, or inputting analog or digital data can be accomplished by transferring data via a computer network from providing entity to acquiring entity. References may also be made to providing, outputting, transmitting, sending, or presenting analog or digital data. In various examples, process of providing, outputting, transmitting, sending, or presenting analog or digital data can be accomplished by transferring data as an input or output parameter of a function call, a parameter of an application programming interface or interprocess communication mechanism. 
     Although discussion above sets forth example implementations of described techniques, other architectures may be used to implement described functionality, and are intended to be within scope of this disclosure. Furthermore, although specific distributions of responsibilities are defined above for purposes of discussion, various functions and responsibilities might be distributed and divided in different ways, depending on circumstances. 
     Furthermore, although subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that subject matter claimed in appended claims is not necessarily limited to specific features or acts described. Rather, specific features and acts are disclosed as exemplary forms of implementing the claims.