Ati Technologies Ulc

Advanced Micro Devices Corporation

Gabor Sines

Ihab Amer

Yang Liu

Lei Zhang

Khaled Mammou

Edward A Harold

Haibo Liu

Wei Gao

Feng Pan

Boris Ivanovic

Mehdi Saeedi

Ying Luo

Crystal Yeong-Pian Sau

Ihab M A Amer

David I Glen

Dong Liu

Layla A Mah

Bruce Montag

Rajabali M Koduri

Mingkai Shao

Fabio Gulino

Ho Hin Lau

Jinbo Qiu

Crystal Sau

Guennadi Riguer

Thomas Daniel Perry

Ian Charles Colbert

Shu-Hsien Wu

Ehsan Mirhadi

Arash Hariri

Michael L Schmit

Daniel Wong

Andy Sung

Omer Irshad

Mohammad Hamed Mousazadeh

Kyle Plumadore

Zhiqi Hao

Samuel D Naffziger

Greg Sadowski

Edward George Callway

Skyler Jonathon Saleh

Ruijin Wu

Carl Kittredge Wakeland

Allen H Rush

Zhen Biao Chen

Arun Sundaresan Iyer

Alexander S Duenas

Jiao Wang

Jitesh Arora

Ahmed M Abdelkhalek

Yubao Zheng

Baochun Li

Pavel Siniavine

Mohamed K Cherif

Benedict Chien

Zhong Hai Cai

Arpit Nitinbhai Patel

Stephen Ho

Catalin Beju

Xingping Cao

Thomas D Perry

Philippe John Louis Yu

Minghao Zhu

Stephen Jiacheng Fu

Zongjie Yan

Kai Sun

Michal Eugeniusz Gallus

Tomasz Stolarczyk

Lawrence A Bair

John-Paul A Compagnone

Gerald S C Chan

Alexander Zorin

Sai Harshita Tupili

Ihab MA Amer

John-Paul Compagnone

Gerald Sc Chan

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.

Machine-learning based collision detection for objects in virtual environments

To leverage an amount of unused bandwidth at a hardware encoder to generate motion estimation data, a processing unit includes a hardware encoder configured to perform a first encoding job including encoder sessions to encode a captured frame, determine motion estimation data for a rendered frame, and encode the rendered frame. Further, the processing unit includes a pre-processing circuitry configured to determine a set of motion estimation parameters based on an encoder delay associated with the performance of the first encoding job by the hardware encoder. The hardware encoder is then configured to perform a second encoding job using the determined set of motion estimation parameters.

Dynamic motion estimation parameter modification for video encoding

A processing apparatus and video encoding method are provided which include receiving a portion of a video sequence and determining complexities for blocks of pixels of the portion of the video sequence. Quantization parameter values for corresponding blocks of pixels are selected based on complexities of the corresponding blocks and visually perceived coding artifacts of the corresponding blocks produced by the quantization parameter values. The blocks of pixels are encoded, using the selected quantization parameter values. The blocks of pixels are decoded and the portion of the video sequence is provided for display.

Quantization parameter map for video encoding with constant perceptual quality

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.

Automated data-driven system to optimize overclocking

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.

Detecting personal-space violations in artificial intelligence based non-player characters

An encoder encodes an image portion by recursively partitioning the portion into a partitioning hierarchy of levels. The top level has a single block representing the entire portion and each lower level has four smaller blocks representing a corresponding larger block at a higher level. A palette table is generated for each bottom-level block based on the pixels of the associated block. For each successively higher level, the encoder generates a palette table for each current-level block by selecting palette colors based on the palette colors from the four palette tables for the associated four blocks at the next-lowest level. A color index map is then generated based on a final palette table selected from the palette tables generated for the partitioning hierarchy. A representation of the portion is then encoded using the final palette table and the color index map to generate a corresponding segment of an encoded bitstream.

Palette mode video encoding utilizing hierarchical palette table generation

A device and method for processing Virtual Reality (VR) data is disclosed. The method comprises transmitting feedback information from the device to a server, wherein the feedback information is captured in the device, receiving data from the server to be presented on the device based on the feedback information, wherein the data includes video data and audio data where the video data is a frame of video data in a sequence of frames and the audio data is the corresponding audio data of the frame, decoding the video data and corresponding audio data of the frame, and controlling the presentation of the video data and corresponding audio data on the device such that the video data is synchronized with the corresponding audio data.

Low latency wireless virtual reality systems and methods

Systems, apparatuses, and methods for performing parallel histogram calculation with application to palette table derivation are disclosed. An encoder calculates a first histogram for a first portion of pixel component value bits of a block of pixels. Then, the encoder selects a first number of the highest pixel count bins from the first histogram. Also, the encoder calculates a second histogram for a second portion of pixel component value bits of the block. The encoder selects a second number of the highest pixel count bins from the second histogram. A third histogram is calculated from the concatenation of bits assigned to the first and second number of bins, and the highest pixel count bins are selected from the third histogram. A palette table is derived based on these highest pixel count bins selected from the third histogram, and the block of pixels is encoded using the palette table.

Parallel histogram calculation with application to palette table derivation

Systems, apparatuses, and methods for calculating multi-pass histograms for palette table derivation are disclosed. An encoder calculates a first histogram for a first portion of most significant bits (MSBs) of pixel component values of a block of an image or video frame. Then, the encoder selects a given number of the highest pixel count bins from the first histogram. The encoder then increases the granularity of these selected highest pixel count bins by evaluating one or more additional bits from the pixel component values. A second histogram is calculated for the concatenation of the original first portion MSBs from the highest pixel count bins and the one or more additional bits, and the highest pixel count bins are selected from the second histogram. A palette table is derived based on these highest pixel count bins selected from the second histogram, and the block is encoded using the palette table.

Hierarchical histogram calculation with application to palette table derivation

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.

Systems and methods for generating remedy recommendations for power and performance issues within semiconductor software and hardware

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.

Assigning bit budgets to parallel encoded video data

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. The trained neural network creates unique music that dynamically varies according to real-time circumstances.

Adaptive audio mixing

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.

Graphics rendering with encoder feedback

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.

Client-side offload of graphics effects processing

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.

Top palette colors selection using sorting for palette mode in video encoding

Growing community of inventors

Markham, Canada

Gabor Sines