Abstract: Systems, apparatuses, and methods for detecting personal-space violations in artificial intelligence (AI) based non-player characters (NPCs) are disclosed. An AI engine creates a NPC that accompanies and/or interacts with a player controlled by a user playing a video game. During gameplay, measures of context-dependent personal space around the player and/or one or more NPCs are generated. A control circuit monitors the movements of the NPC during gameplay and determines whether the NPC is adhering to or violating the measures of context-dependent personal space. The control circuit can monitor the movements of multiple NPCs simultaneously during gameplay, keeping a separate score for each NPC. After some amount of time has elapsed, the scores of the NPCs are recorded, and then the scores are provided to a machine learning engine to retrain the AI engines controlling the NPCs.

Abstract: A processing device includes an automated overclocking system and a processor. The automated overclocking system is data-driven and includes an inference engine that executes a machine learning model configured to generate a first output based on a current configuration of the processing device. The first output includes a first set of overclocking parameters. The processor is configured to adjust one or more operating characteristics of at least one component of the processing device based on the first set of overclocking parameters.

Abstract: An apparatus and method for efficiently creating less computationally intensive nodes for a neural network. In various implementations, a computing system includes a memory that stores multiple input data values for training a neural network, and a processor. Rather than determine a bit width P of an integer accumulator of a node of the neural network based on bit widths of the input data values and corresponding weight values, the processor selects the bit width P during training. The processor adjusts the magnitudes of the weight values during iterative stages of training the node such that an L1 norm value of the weight values of the node does not exceed a corresponding weight magnitude limit.

Abstract: A system and method for an interactive three piece microphone with customizable features that may be quickly accessed by a user whereby the users' settings are automatically uploaded to the microphone providing a more personalized experience. The microphone may be used in a variety of situations where users may challenge one another as well as collect monetary rewards based on the location and type of performance they have.

Abstract: The disclosed computer-implemented method for generating remedy recommendations for power and performance issues within semiconductor software and hardware. For example, the disclosed systems and methods can apply a rule-based model to telemetry data to generate rule-based root-cause outputs as well as telemetry-based unknown outputs. The disclosed systems and methods can further apply a root-cause machine learning model to the telemetry-based unknown outputs to analyze deep and complex failure patterns with the telemetry-based unknown outputs to ultimately generate one or more root-cause remedy recommendations that are specific to the identified failure and the client computing device that is experiencing that failure.

Abstract: The disclosed computer-implemented method for generating remedy recommendations for power and performance issues within semiconductor software and hardware. For example, the disclosed systems and methods can apply a rule-based model to telemetry data to generate rule-based root-cause outputs as well as telemetry-based unknown outputs. The disclosed systems and methods can further apply a root-cause machine learning model to the telemetry-based unknown outputs to analyze deep and complex failure patterns with the telemetry-based unknown outputs to ultimately generate one or more root-cause remedy recommendations that are specific to the identified failure and the client computing device that is experiencing that failure.

Abstract: Methods and devices are provided for processing data using a neural network. Activations from a previous layer of the neural network are received by a layer of the neural network. Weighted values, to be applied to values of elements of the activations, are determined based on a spatial correlation of the elements and a task error output by the layer. The weighted values are applied to the values of the elements and a combined error is determined based on the task error and the spatial correlation.

Abstract: Shader source code performance prediction is described. In accordance with the described techniques, an update to shader source code for implementing a shader is received. A prediction of performance of the shader on a processing unit is generated based on the update to the shader source code. Feedback about the update is output. The feedback includes the prediction of performance of the shader. In one or more implementations, generating the prediction of performance of the shader includes compiling the shader source code with the update to generate a representation of the shader, inputting the representation of the shader to one or more machine learning models, and receiving the prediction of performance of the shader as an output from the one or more machine learning models.

Abstract: Systems, apparatuses, and methods for detecting personal-space violations in artificial intelligence (AI) based non-player characters (NPCs) are disclosed. An AI engine creates a NPC that accompanies and/or interacts with a player controlled by a user playing a video game. During gameplay, measures of context-dependent personal space around the player and/or one or more NPCs are generated. A control circuit monitors the movements of the NPC during gameplay and determines whether the NPC is adhering to or violating the measures of context-dependent personal space. The control circuit can monitor the movements of multiple NPCs simultaneously during gameplay, keeping a separate score for each NPC. After some amount of time has elapsed, the scores of the NPCs are recorded, and then the scores are provided to a machine learning engine to retrain the AI engines controlling the NPCs.

Abstract: An apparatus includes a processor configured to determine a first distribution associated with an artificial agent based on behavior associated with the artificial agent and a second distribution based on behavior of a user. The processor is further configured to generate a human-likeness similarity measurement by comparing the first distribution to the second distribution and modify the behavior of the artificial agent in response to the similarity measurement failing to satisfy a similarity threshold.

Abstract: Shader source code performance prediction is described. In accordance with the described techniques, an update to shader source code for implementing a shader is received. A prediction of performance of the shader on a processing unit is generated based on the update to the shader source code. Feedback about the update is output. The feedback includes the prediction of performance of the shader. In one or more implementations, generating the prediction of performance of the shader includes compiling the shader source code with the update to generate a representation of the shader, inputting the representation of the shader to one or more machine learning models, and receiving the prediction of performance of the shader as an output from the one or more machine learning models.

Abstract: Described herein are techniques for generating a compiled shader program. The techniques include identifying input features of a shader program, providing the identified input features of the shader program to a trained model for selecting compiler operation values for shader programs, receiving, as output from the trained model, a compiler operation value for the shader program, and generating a compiled shader program based on the compiler operation value for execution on one or more compute units.

Abstract: Methods and apparatus are disclosed which are useful in rapidly verifying a person's identity at a distance, and/or identifying a person with a high degree of certitude, from a pool of other persons, using an innovative technique to compare facial images. Such a person (Candidate) may voluntarily consent or cooperate to have his facial image recorded, or the Candidate's facial image may have been recorded involuntarily. Typically, an image recorded for identification purposes will be a facial image of a Candidate present before a camera or other image procuring device, to be compared with a previously stored Reference facial image, as in a building lobby, for identification purposes. The Candidate's facial image is recorded voluntarily but the Reference image is not in the Candidate's possession.

Abstract: A system (method and apparatus) for verifying/identifying a person based on contour of the knuckle surface of at least one hand, e.g. a digitized waveform unique to each individual. At an Enrollment Station a microcomputer is connected to a device for identifying the knuckle surface profile (e.g. a video camera or electromechanical contour sensing device). A candidate user grasps a grip handle, preferably vertically oriented, on the apparatus, positioning a fist before a viewing window and activating the device to scan or assess the fist and generate a contour of the user's knuckle surface contour. User's data comprises a knuckle contour, an assigned PIN, and optionally, information such as user's name, bank ID number, Social Security Number, and access restrictions. User's data may be stored as a profile (template) in an ID card, and/or in a master database containing data of all authorized users in a guarded system.

Abstract: A method and apparatus for verifying/identifying a person based on contour of the knuckles of at least one hand, e.g. a digitized waveform unique to each individual. At an Enrollment Station a microcomputer is connected to a video camera (with CCD array). A candidate user grasps and turns a grip handle on the apparatus, thus positioning a fist before a viewing window and activating the camera to scan the fist and generate a contour of the user's knuckle profile. User's data comprises a knuckle profile of fist(s), an assigned PIN, and optionally, information such as user's name, bank ID number, Social Security Number, and access restrictions. User's data may be stored as an electronic waveform profile (template) in an ID card, and simultaneously in a master database containing data of all authorized users in a guarded system (factory, retail store, bank customers, computer data files). Similar apparatus is installed at each Guarded Station (a door, turnstile, gate, ATM, etc.).

Abstract: A method of and apparatus for verifying the identity of an individual is based on the profile of a hand print. A set of reference data is created by determining the location of the edges of each finger separately to generate a set of finger profiles, establishing a central axis for each finger profile by reference to the edges thereof, rotating the central axis of each finger profile until it is normal to a reference line, determining the width of each finger profile at predetermined locations parallel to said reference line, and recording said finger profile information. Thereafter, a candidate's finger profile can then be compared with the reference profile by comparing the width of each finger profile at predetermined locations, determining whether the corresponding between finger widths at said predetermined locations meets a predetermined criteria, and then indicating acceptance or rejection of the candidate.