Patent Publication Number: US-2023147096-A1

Title: Unstructured data storage and retrieval in conversational artificial intelligence applications

Description:
BACKGROUND 
     Interaction environments may include conversational artificial intelligence systems that receive a user input, such as a voice input or textual 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. Typically, data associated with these systems, such as intent/slot data sets, are stored in structured data schema, which further creates problems with additions or changes. 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 input classification, storage, and retrieval, according to at least one embodiment; 
         FIG.  3    illustrates an example environment for classification, storage, and retrieval, according to at least one embodiment; 
         FIG.  4    illustrates an example interface for an interaction environment, according to at least one embodiment; 
         FIG.  5 A  illustrates an example flow chart of a process for input classification, according to at least one embodiment; 
         FIG.  5 B  illustrates an example flow chart of a process for input classification and retrieval, according to at least one embodiment; 
         FIG.  6    illustrates an example flow chart of a process for generating a response according to an input, 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 unstructured storage and retrieval of information, such as information utilized with interaction environments. In at least one embodiment, systems and methods are used with chat bots or conversational artificial intelligence (AI) systems in order to store and retrieval data that may be stored with differed storage schemas and/or without a structured storage schema response to a query. Various embodiments may include one or more classifiers to analyze a user input, determine whether the input is associated with an information-based request, and then direct the input along the appropriate pipeline for analysis, and response. In at least one embodiment, an information-based request may be evaluated using one or more extractive question answer models in order to identify one or more features within the input and determine, from a set of unstructured data, information responsive to the input. The information may then be presented to the user or may be utilized to execute one or more actions, among other options. Moreover, systems and methods may be used to store user inputs as unstructured text, such as storage in a natural-language method, for later retrieval. In this manner, data sets may be easily updated and maintained without the rigidity of conforming each piece of information to a request schema. 
     Various embodiments may be used, in part, with conversational AI systems that provide information or execute commands responsive to one or more user inputs. In at least one embodiment, a user may provide an input to the environment, such as a request for information or a request to perform one or more tasks. The system may process the input, such as processing an audio input using one or more natural language or speech recognition systems, or may evaluate a textual input to classify the input as belonging to one or more categories. In at least one embodiment, categories may be associated with an information-based input, an intent/slot input, a declaratory input, and others. Depending on the classification, a different processing pipeline may be utilized for the input. By way of example only, an information-based input may be evaluated by one or more extractive question answer models against a data store of unstructured data, where the model may determine or more features from the input to generate a response to the input. As another example, an intent/slot classification may be directed toward a pipeline where a trained extractive question answer model evaluates the input against different intent/slot information in order to populate a slot with an appropriate value to provide a response. As a further example, a declaratory input may be classified and then added to an unstructured data repository, where it may then be utilized or be made available to later user queries. 
     Various embodiments may be utilized to provide a response to a user input, which may be in the form of an auditory input, a textual input, a selective input (e.g., selecting a content element), or an instructional input, such as a data file that executes one or more operations within the interaction environment. Systems and methods may not only store relevant information as natural text in unstructured memory and answer flexible questions based on the information, but moreover, may retrieve pieces of information to use in commands. For example, a result may be associated with a textural or voice response to a user, as well as or additionally, fulfillment of one or more actions connected to the result. By way of example, one or more meta commands may be added to a responsive output associated with the input, where the command itself is not provided to the user, but the command triggers one or more additional actions. 
     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, selection of one or more content elements, 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  as well as to present one or more queries, such as a question associated with information within the environment. 
     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 classify the input, determine an appropriate response to the input, and then execute the appropriate response. 
     Systems and methods may be directed toward storing, retrieving, and updating unstructured text. Embodiments include storing information related to a conversional AI where a user presents a query, the query is evaluated to determine whether it is information based, and then a question-answer neural network model is used to extract facts from the query in order to determine a response from unstructured text. Answers or data to various queries may be stored naturally as unstructured text, rather than in an intent/slot schema that may be difficult to generate and/or update. During operation, an input query is directed toward a classifier that determines whether the query is a question. Moreover, the question is broken into an information based query or an intent/slot query to be directed toward the appropriate pipeline. An extractive QA model may be trained and then used to provide a response to the information based query, such as by searching through the unstructured text to identify an answer to the input query. The system enables development of a conversational AI that is supported with unstructured memory, which may increase custom facts associated with the system. 
     Embodiments of the present disclosure may provide one or more improvements over existing systems that utilize a structured data schema to store, update, and retrieve information. By way of example, the unstructured, natural language storage of the present embodiments may provide improvements over intent/slot schemas where particular responses or intents are pre-loaded and defined for use with the system. Accordingly, intent/slot schemas are typically generated by looking at a variety of different inputs and desired outputs in order to create intent/slot combinations that are then identified and executed responsive to the input. Generation of these systems may be time consuming and are not flexible to user inputs that do not correspond to the preloaded intents and slots. Similarly, systems provide improvements over variable dictionaries and knowledge graphs that may require rigid classification of information, rather than storage and retrieval of unstructured text. 
     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  200  is provided to a classifier  204 , which may be part of a distributed system or a locally hosted classifier, among other options. The classifier  204  may include one or more trained machine learning systems that evaluate one or more aspects of the input to determine whether the input is associated with a question or not. By way of example only, one or more punctuation models, which may be part of one or more Natural Language Processing (NLP) models, may be utilized to predict whether a punctuation mark follows a word, and moreover, to predict whether an input statement or phrase is a question. Furthermore, at least portions of the classifier  204  may incorporate one or more natural language understanding (NLU) systems that 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. 
     In operation, the input  202  is evaluated by the classifier  204  and, based at least in part on a classification of the input  202 , data may be transferred along one or more pipelines for further processing. In this example, a question environment  206  may include evaluations based on both information-based queries  208  and intent/slot queries  210 , among other options. For example, the classifier  204  may initially determine the input  202  corresponds to a question and direct data along an appropriate pipeline to the question environment  206 . However, within the question environment, one or more additional analysis or determinations may be performed in order to determine whether appropriate processing is performed by the information-based system  208  or the intent/slot system  210 , which as noted above, are examples as there may be additional systems utilized within the question environment  206 . By way of example only, one or more functions may be utilized to evaluate and determine how to process an input, as shown below: 
     
       
         
           
               
               
               
               
             
               
                   
                   
               
             
            
               
                   
                   
                 if question(query): 
                   
               
               
                   
                   
                  response = question_answering(query) 
                   
               
               
                   
                   
                 if not response: 
                   
               
               
                   
                   
                  intent, score = recognize_intent(query) 
                   
               
               
                   
                   
                  if intent and score &gt; Threshold: #intent recognized 
                   
               
               
                   
                   
                   slot = recognize_slot(query, slot question) 
                   
               
               
                   
                   
                   slot = check_slot_value(slot, supported slot values) 
                   
               
               
                   
                   
                   response, command = execute_command(intent, slot) 
                   
               
               
                   
                   
                 tts.say(response) 
               
               
                   
                   
               
            
           
         
       
     
     In this example, the initial query is evaluated and processed using the trained extractive question answering network if the query is a question and if such a query can be processed by the system. However, in other examples, the intent/slot system  210  may proceed with identifying an intent, identifying an associated slot, populating the slot, and then providing a response. 
     Additionally, the input  202  may further be classified as being a declarative statement and may be directed toward an information storage system  212 . For example, the user may provide, as the input  202 , an affirmative statement such as “My favorite color is green” or “This should be the default view setting.” This information may then be provided to the information storage system  212  for storage and retention as unstructured natural language, which may then be utilized as a response to another query. As will be appreciated, storage of the input  202  as unstructured natural language enables storage and retrieval in real and near-real time such that data associated with the system may be updated at runtime without retraining the model or modifying intent/slot classifications by adding new intents. In this manner, the conversational AI may be updated more frequently and in a more natural way, using natural language, rather than requiring information to conform to specific data structures. 
     An environment  300  may be utilized with one or more conversational Ais, as shown in  FIG.  3   . It should be appreciated that the environment  300  may include more or fewer components and that various components of the environment  300  may be incorporated into singular systems, but may be shown as separate modules for convenience and clarity. In this example, an input  302  is transmitted to a conversational system  304  via one or more networks  306 . The networks  306  may be wired or wireless networks which include one or more intermediate systems, such as user devices, server components, switches, and the like. Moreover, it should be appreciated that one or more features of the conversational system  304  may be pre-loaded or otherwise stored on a user device such that transmission of at least a portion of data may not utilize the network  306  but may be performed locally on a device. 
     In this example, an input processor  308  receives the input  308  and may perform one or more pre- or post-processing steps. For example, input processor  308  may include one or more NLP systems that evaluate an auditory input to extract one or more features from the input, among other options. Furthermore, in embodiments, input processor  308  may include a text processing system for preprocessing (e.g., tokenization, removal of punctuation, removal of stop words, stemming, lemmatization, etc.), feature extraction, and the like. It should be appreciated that the input processor  308  may utilize one or more trained machine learning systems and may further be incorporated into other components of the conversational system  304 . 
     A classifier  310  may be used to determine whether the input corresponds to a question, a statement, or any other label. For example, the classifier  310  may utilize one or more trained machine learning systems to evaluate whether an input is in the form of a question, for example using a punctuation model, among other potential models. As noted above, the classifier  310  may then direct the input along different pathways depending on a respective classification, where questions may be further evaluated against one or more data bases to determine a response and statements may be evaluated and added to a corpus of unstructured text. 
     As noted above, questions may be directed toward a question environment where one or more extractive question answer models  312  are used to determine a response to the input. By way of example, the extractive question answer model  312  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 “what colors can I paint the car” unstructured text may be evaluated to identify potential colors for the car, where those colors may then be presented to the user. For example, if unstructured text included natural language information such as “car colors are white, black, red, yellow, and gray” then the response to the question would be “white, black, red, yellow, and gray.” Additionally, it should be appreciated that the model  312  may also be utilized with intent/slot evaluations. In various embodiments, the extractive question answer model may be a trained neural network system, such as Megatron from NVIDIA Corporation. 
     In various embodiments, training data  314  may be utilized to train the model  312 , where the data includes a corpus of information, such as the multiQA dataset. As a result, the model  312  may be capable of extracting relevant facts directly from a corpus of unstructured text  316 , which corresponds to information provided for the conversational system  304 . By way of example, the corpus  316  may include information presented as natural language, such as sentences, paragraphs, CSV data, and the like. Furthermore, the corpus  316  may further include one or more structure datasets. 
     The illustrated embodiment also includes a runtime interaction module  318  to identify and incorporate different statements or facts that may be utilized to update the corpus  316 . By way of example, the classifier  310  may determine that an input is not associated with a question and may provide the input to the runtime interaction module  318  for evaluation, for example using one or more machine learning systems. One or more features may be identified and/or extracted from the input in order to update the corpus  316 . For example, the input may correspond to a user preference, such as an utterance that the user prefers a certain color or camera angle. This information may then be utilized to update the corpus  316  such that future commands or requests may incorporate the user&#39;s preferences. In at least one embodiment, a data modifier  320  may be utilized to update the corpus  316 , such as formatting the input in natural language format. 
     Various embodiments may also cause one or more actions to be performed that are associated with the input. For example, an action module  322  may be used to implement one or more meta commands associated with natural language text that enables connection to appropriate commands associated with the text. In various embodiments, the meta commands may be a symbol or call to the machine learning systems to ignore or otherwise disregard certain characters, where those characters are associated with the actions. In various embodiments, the action can be executed in parallel or semi-parallel with a response to the input. In at least on embodiment, meta commands may be separate sentences or strings of characters that follow symbol or call. For example, if the user were to ask, “What side dishes come with this meal?” an associated action may be to not only provide a verbal or textual response to the user to answer the question, but to also provide pictures or display a list. As a result, within the unstructured text, the symbol or call may follow the unstructured text associated with the answer such that when the answer to the user input is identified, one or more actions also executes. 
     As noted here, various embodiments enable storage and retrieval of information as natural language, unstructured text. Accordingly, new information may be easily added to the corpus of information without formatting to a particular schema. A storage system  400  may include a set of information  402 , as illustrated in  FIG.  4   . The set of information  402  is stored as free text in a natural language format, which in this case is a series of sentences. It should be appreciated that different unstructured memory schema may be used, such as lists (e.g., colors are white, black, red, yellow, and grey), key value pairs (e.g., colors:white, black, red, yellow, grey), and the like. Furthermore, it should be appreciated that different structured schema may also be utilized with the storage system  400  within the set of information  402 . In other words, different schema may be combined within the storage system  400  as the set of information  402 , thereby providing improved flexibility for storing and updating information. 
     In this example, an input  404  shows an example of a user query provided to the system  400 , which in this instance is a voice input that has been converted to text, for example using one or more NLP systems. It should be appreciated that the input  404  is provided as an example to illustrate the process evaluated by the system  400  and that, in embodiments, the user utilizing the system  400  will not visualize the set of information  402  and/or the input  404 . That is, the system  400  may execute in the background while a different user interface is shown to the user. In this example, the input  404  corresponds to a question, which may be identified by one or more classifiers, are described above. Moreover, in various embodiments, the question may be further analyzed to determine whether it is an information-based question. In this instance, the query is related to a question regarding capabilities of the system, which may correspond to an information-based question. 
     In at least one embodiment, a generative response  408  may be enabled such that the answer  406  is provided in a sentence structure to the user. By way of example, a generative neural network may be utilized to receive, as an input, the answer  406  and then to determine an appropriate response incorporating the answer  406 . In this example, the generative response  408  provides the answer  406  in a sentence format to the user. As will be appreciated, using the generative response  408  may provide an improved interaction experience for the user, where the user may feel as if they are engaging in a conversation with the system, as opposed to receiving only the information. Accordingly, the user may be encouraged to use the system for more purposes. 
     In at least one embodiment, one or more actions may be tied to different user inputs, where the action may include a marker or call, as noted above. An action set  410  may include a list or set of associated actions with different answers  406 . In the example of  FIG.  4   , there are no related actions associated with the user&#39;s request to learn about color options. However, in various embodiments, actions could be associated with the request, such as showing a panel or swatch of the color options. Accordingly, different calls or functions may be listed. In at least one embodiment, a provider may access the system  400  to make changes or updates. For example, different information may be added to the set of information  402  and/or different actions may be correlated to different questions or situations. In this manner, dynamic changes to the system may be provided at runtime without retraining the system. 
       FIG.  5 A  illustrates an example process  500  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 may be received at an interaction environment  502 . In various embodiments, the input may be a voice input, a textual input, a command from a script of software program, a selection of a content element, or the like. A classification of the input may be determined  504 , for example using one or more machine learning systems that determine whether the input is a question or a declarative statement  506 . As noted above, the determination may include, at least in part, one or more models, such as a punctuation model. 
     If the information is a declarative statement, such as the user providing information, the input may be stored in a natural language format  508 . Storing the input may enable later identification and retrieval of the information, for example, if the user provides information that may be useful for an interaction environment, such as verifying one or more preferences. Furthermore, as discussed herein, storing the information in a natural language format provides flexibility to the system such that a certain storage schema may not be required, which my enable faster, automated storage of the newly provided information. 
     In at least one embodiment, the input is a question, and one or more text sequences may be extracted from the input  510 . The extracted portions of the text sequence may be provided to one or more machine learning systems, such as an extractive question answer model, to determine, based at least in part on the sequence, a response  512 . The response may provide an answer to the question posed by the input, where the response may be identified within a set of information stored in an unstructured format, such as a natural language format. The response may then be used to generate a reply to the input  514 , such as providing additional information, executing an action, or a combination thereof. 
       FIG.  5 B  illustrates an example process  520  for responding to a user input. In this example, an input is received at an interaction environment  522 . As noted, the input may include one or more queries that are provided via a voice interaction, textual input, selection of a content element, or other options. In at last one embodiment, the input may include an information-based question  524 . For example, the input may be a question regarding the potential capabilities of a conversational AI system. Information from the input may be utilized to evaluate data stored as unstructured natural language in order to determine a response to the input  526 . By way of example, an extractive question answer model may take, as an input, one or more features from the input to determine whether information within the stored data is responsive to the input. A reply may then be generated using the response  528 . 
       FIG.  6    illustrates an example process  600  for executing an action based on an input. In this example, a set of information is stored as unstructured, natural language  602 . For example, the information may be stored as a series of sentences, among other options. An action corresponding to a portion of the information is determined  604 . The action may include one or more capabilities of an interaction environment, such as providing a visual indication responsive to a user query. A call function may be assigned to the portion of information, where the call function is used to execute an action  606 . The call function, in various embodiments, may include a symbol or indicator so that the call function is not counted or included within the set of information. 
     In various embodiments, an input is received and the portion is retrieved responsive to the input  608 . The portion may be used to generate a response to the input  610  and, based on the response, one or more associated actions may be executed  612 . In this manner, the call associated with the portion may be executed in parallel with providing the response. 
     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 storing and retrieving information 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 storing and retrieving information 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 storing and retrieving information 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 storing and retrieving information 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 storing and retrieving information 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.