Ati Technologies Ulc

Advanced Micro Devices Corporation

Mehdi Saeedi

Boris Ivanovic

Gabor Sines

Thomas Daniel Perry

Arash Hariri

Ihab Amer

Yang Liu

Khaled Mammou

Edward George Callway

Lei Zhang

Carl Kittredge Wakeland

Haibo Liu

Allen H Rush

Guennadi Riguer

Ihab M A Amer

Steven J Tovey

Zhuo Chen

Ho Hin Lau

Mingkai Shao

Ian Charles Colbert

Mehdi Semsarzadeh

Andrej Zdravkovic

Thomas D Perry

Sai Harshita Tupili

Tomasz Stolarczyk

Konstantin Moskvitin

Michal Eugeniusz Gallus

A technique for performing video operations is provided. The technique includes decoding underlying content to obtain a decoded block; and applying a shade pattern to the decoded block to obtain a final block.

Out-of-the-loop shade elimination technique

Systems, apparatuses, and methods for updating and optimizing task scheduling policies are disclosed. A new policy is obtained and updated at runtime by a client based on a server analyzing a wide spectrum of telemetry data on a relatively long time scale. Instead of only looking at the telemetry data from the client's execution of tasks for the previous frame, the server analyzes the execution times of tasks for multiple previous frames so as to determine a more optimal policy for subsequent frames. This mechanism enables making a more informed task scheduling policy decision as well as customizing the policy per application, game, and user without requiring a driver update. Also, this mechanism facilitates improved load balancing across the various processing engines, each of which has their own task queues. The improved load balancing is achieved by analyzing the telemetry data including resource utilization statistics for the different processing engines.

Updating shader scheduling policy at runtime

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

A method includes obtaining, at a data reduction module, metrics of a first block of an input video frame and a second block of a reference frame. The data reduction module includes an analysis module and a filter. A perceptual importance of the first block of the input video frame is determined at the analysis module using the metrics. An entropy of the input video frame provided to an encoder is adjusted at the filter of the data reduction module based on the perceptual importance of the first block of the input video frame.

Metric driven adaptive processing for video encoders

Systems, apparatuses, and methods for implementing spatial block-level pixel activity extraction optimization leveraging motion vectors are disclosed. Control logic coupled to an encoder generates block-level pixel activity metrics for a new frame based on the previously calculated block-level pixel activity data from a reference frame. A cost is calculated for each block of a new frame with respect to a corresponding block of the reference frame. If the cost is less than a first threshold, then the control logic generates an estimate of a pixel activity metric for the block which is equal to a previously calculated pixel activity metric for a corresponding block of the reference frame. If the cost is greater than the first threshold but less than a second threshold, an estimate of the pixel activity metric is generated by extrapolating from the previously calculated pixel activity metric.

Spatial block-level pixel activity extraction optimization leveraging motion vectors

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

A processing system estimates motion between blocks of images based on differences in pixel activities between blocks. Blocks having similar pixel activities are more likely to be matches for motion estimation than are blocks having dissimilar pixel activities. Accordingly, the processing system compares pixel activity between current block of a current image and candidate blocks within a search area in the reference image, and estimates motion based on a subset of candidate blocks having a difference in pixel activity from that of the current block within a threshold.

Motion estimation using pixel activity metrics

A multimedia system allocates, during encoding of a multimedia stream, bits to portions of frames based on quality metrics and bit usages for different quantization parameters (QPs). An encoder of the multimedia system encodes a frame in a first pass with a first QP and in a second pass with a second QP. A comparator of the multimedia system measures and compares quality metrics, such as mean squared error, for each portion of the frame encoded with the first QP and the second QP. The comparator compares the difference between the quality metrics for each portion encoded with each QP to a threshold. If the difference in quality metrics for a portion exceeds the threshold, the comparator selects the portion for inclusion in a subset of portions to be encoded with the second QP.

Benefit-based bitrate distribution for video encoding

An encoder calculates a first local encoding parameter for a first block of video content based on one or more local metrics. The encoder modifies the first local encoding parameter based on one or more second local encoding parameters for one or more second blocks of video content that are adjacent to the first block of video content. The encoder then encodes the first block using the modified first local encoding parameter. In some cases, the local encoding parameters are quantization parameters used to quantize values of pixels or compression parameters used to compress values of the pixels. The local metric can include one or more of a target bit rate, a texture complexity, a contrast, an indicator of motion in the first block, and an importance map.

Consistent adjustment of parameters for video encoding

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

Systems, apparatuses, and methods for bit budgeting in video encode pre-analysis based on context and features are disclosed. A pre-encoder receives a video frame and evaluates each block of the frame for the presence of several contextual indicators. The contextual indicators can include memory colors, text, depth of field, and other specific objects. For each contextual indicator detected, a coefficient is generated and added with other coefficients to generate a final importance value for the block. The coefficients can be adjusted so that only a defined fraction of the picture is deemed important. The final importance value of the block is used to determine the bit budget for the block. The block bit budgets are provided to the encoder and used to influence the quantization parameters used for encoding the blocks.

Video encode pre-analysis bit budgeting based on context and features

Systems, methods, and techniques utilize reinforcement learning to efficiently schedule a sequence of jobs for execution by one or more processing threads. A first sequence of execution jobs associated with rendering a target frame of a sequence of frames is received. One or more reward metrics related to rendering the target frame are selected. A modified sequence of execution jobs for rendering the target frame is generated, such as by reordering the first sequence of execution jobs. The modified sequence is evaluated with respect to the selected reward metric(s); and rendering the target frame is initiated based at least in part on the evaluating of the modified sequence with respect to the one or more selected reward metric(s).

Job scheduling using reinforcement learning

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

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.

Feature reordering based on similarity for improved memory compression transfers during machine learning jobs

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.

Growing community of inventors

Markham, Canada

Mehdi Saeedi