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: A technique for encoding video is provided. The technique includes for a first portion of a first frame that is encoded by a first encoder in parallel with a second portion of the first frame that is encoded by a second encoder, determining a historical complexity distribution; determining a first bit budget for the first portion of the first frame based on the historical complexity distribution; and encoding the first portion of the first frame by the first encoder, based on the first bit budget.

Abstract: Adaptive decoder-drive encoder reconfiguration techniques are described. In one example, techniques include detecting an operational condition at a consumer using a sensor, the consumer receiving a communication of digital content from an encoder; generating an adaptation instruction by the decoder based on the detecting; transmitting the adaptation instruction by the decoder for receipt by the encoder; and receiving an adapted communication of the digital content generated by the encoder, the adapted communication caused by reconfiguration of the encoder based on the adaptation instruction received from the decoder.

Abstract: Systems, apparatuses, and methods for performing adaptive audio mixing are disclosed. A trained neural network dynamically selects and mixes pre-recorded, human-composed music stems that are composed as mutually compatible sets. Stem and track selection, volume mixing, filtering, dynamic compression, acoustical/reverberant characteristics, segues, tempo, beat-matching and crossfading parameters generated by the neural network are inferred from the game scene characteristics and other dynamically changing factors. The trained neural network selects an artist's pre-recorded stems and mixes the stems in real-time in unique ways to dynamically adjust and modify background music based on factors such as game scenario, the unique storyline of the player, scene elements, the player's profile, interest, and performance, adjustments made to game controls (e.g., music volume), number of viewers, received comments, player's popularity, player's native language, player's presence, and/or other factors.

Abstract: A feedback processing module includes a memory configured to store feedback received from an encoder. The feedback includes parameters associated with encoded graphics content generated by a graphics engine. The feedback processing module also includes a processor configured to generate configuration information for the graphics engine based on the feedback. The graphics engine is configured to execute a workload based on the configuration information. In some cases, the feedback processing module is also configured to receive feedback from a decoder that is used to decode the graphics content that is encoded by the encoder and generate the configuration information based on the feedback received from the decoder.

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: A server offloads graphics effects processing to a client device with graphics processing resources by determining a modification to a graphics effects operation, generating a portion of a rendered video stream using the modification to the graphics effects operation, and providing an encoded representation of the portion of the rendered video stream to the client device, along with metadata representing the modification implemented. The client device decodes the encoded representation to recover the portion of the rendered video stream and selectively performs a graphics effects operation on the recovered portion to at least partially revert the resulting graphics effects for the portion to the intended effects without the modification implemented by the server.

Abstract: Virtual Reality (VR) processing devices and methods are provided for transmitting user feedback information comprising at least one of user position information and user orientation information, receiving encoded audio-video (AN) data, which is generated based on the transmitted user feedback information, separating the A/V data into video data and audio data corresponding to a portion of a next frame of a sequence of frames of the video data to be displayed, decoding the portion of a next frame of the video data and the corresponding audio data, providing the audio data for aural presentation and controlling the portion of the next frame of the video data to be displayed in synchronization with the corresponding audio data.

Abstract: An encoding method is provided which includes receiving a plurality of images, obtaining values of elements in a portion of the images, sorting the elements according to different values of the elements, sorting the elements according to a number of occurrences of the different values and encoding the elements using a subset of the different values having corresponding numbers of occurrences that are higher than corresponding numbers of occurrences of other values. Examples also include a processing device and method for use with palette mode encoding in which the elements are a portion of pixels in images and the values are color values of the portion of pixels in the images.

Abstract: Calculating, for each frame of a plurality of frames, a corresponding quality value; calculating, for each frame of the plurality of frames, based on one or more visual attributes of a frame, a weight for the corresponding quality value of the frame; calculating an aggregate quality value for the plurality of frames based on the weight and the corresponding quality value for each frame of the plurality of frames; and providing an assessment of the plurality of frames based on the aggregate quality value for the plurality of frames.

Abstract: A processing device for executing a machine learning neural network operation includes memory and a processor. The processor is configured to receive input data at a layer of the machine learning neural network operation, receive a plurality of sorted filters to be applied to the input data, apply the plurality of sorted filters to the input data to produce a plurality of different feature maps, compress the plurality of different feature maps according to a similarity of the feature maps relative to each other and store the plurality of different feature maps in the memory.

Abstract: A processing device for executing a machine learning neural network operation includes memory and a processor. The processor is configured to receive input data at a layer of the machine learning neural network operation, receive a plurality of sorted filters to be applied to the input data, apply the plurality of sorted filters to the input data to produce a plurality of different feature maps, compress the plurality of different feature maps according to a sparsity of the feature maps and store the plurality of different feature maps in the memory.

Abstract: Virtual Reality (VR) processing devices and methods are provided for transmitting user feedback information comprising at least one of user position information and user orientation information, receiving encoded audio-video (A/V) data, which is generated based on the transmitted user feedback information, separating the A/V data into video data and audio data corresponding to a portion of a next frame of a sequence of frames of the video data to be displayed, decoding the portion of a next frame of the video data and the corresponding audio data, providing the audio data for aural presentation and controlling the portion of the next frame of the video data to be displayed in synchronization with the corresponding audio data.

Abstract: Methods and devices are provided for encoding video. By using co-sited gradient and variance values to detect text and line in frames of the video. A processor is configured to receive a plurality of frames of video, determine, for a portion of a frame, a variance of the portion of the frame and a gradient of the portion of the frame and encode, using one of a plurality of different encoding qualities, the portion of the frame based on the gradient and the variance of the portion of the frame. Encoding is performed at both the sub-frame level and frame level. The portion of the frame is classified into one of a plurality of categories based on the gradient and variance and encoded based on the category.

Abstract: Methods and apparatus provide cloud-based video encoding that generates encoded video data by one or more encoders in a cloud platform for a plurality of cloud encoding sessions. The methods and apparatus generate operational improvement tradeoff data in response to operational encoding metrics associated with the one or more encoders and change operational characteristics of the one or more encoders for at least one of the cloud encoding sessions based on the operational improvement tradeoff data.

Abstract: An apparatus includes a processor and a collision detection unit operatively coupled to the processor. The collision detection unit is configured to process, using a machine learning model, one or more parameters associated with a ray cast in virtual environment comprising an object. The machine learning model is configured to approximate a mesh representing the object. The collision detection unit is further configured to determine if the ray collides with the object based on processing the one or more parameters. In response to determining if the ray collides with the object, the collision detection unit is configured to generate collision data associated with the ray and the object.

Abstract: Systems, apparatuses, and methods for creating human-like non-player character (NPC) behavior with reinforcement learning (RL) are disclosed. An artificial intelligence (AI) engine creates a NPC that has seamless movement when accompanying a player controlled by a user playing a video game. The AI engine is RL-trained to stay close to the player but not get in the player's way while acting in a human-like manner. Also, the AI engine is RL-trained to evaluate the quality of information that is received over time from other AI engines and then to act on the evaluated information quality. Each AI agent is trained to evaluate the other AI agents and determine whether another AI agent is a friend or a foe. In some cases, groups of AI agents collaborate together to either help or hinder the player. The capabilities of each AI agent are independent from the capabilities of other AI agents.

Abstract: Methods and devices are provided for encoding video. By using co-sited gradient and variance values to detect text and line in frames of the video. A processor is configured to receive a plurality of frames of video, determine, for a portion of a frame, a variance of the portion of the frame and a gradient of the portion of the frame and encode, using one of a plurality of different encoding qualities, the portion of the frame based on the gradient and the variance of the portion of the frame. Encoding is performed at both the sub-frame level and frame level. The portion of the frame is classified into one of a plurality of categories based on the gradient and variance and encoded based on the category.

Abstract: Mapping machine learning activation data to a representative value palette, including: selecting, from a plurality of activation values of a model execution, a plurality of representative values; identifying, for each activation value of the plurality of activation values, a representative value of the plurality of representative values; calculating, for each activation value of the plurality of activation values, a corresponding residual value as a difference between an activation value and a corresponding representative value; and storing, for each activation value of the plurality of activation values, the corresponding residual value and an index of the corresponding representative value.