公开(公告)号：US20240111620A1

公开(公告)日：2024-04-04

申请号：US17958116

申请日：2022-09-30

Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

Inventor： Mohammad Hamed Mousazadeh ,  Arpit Patel ,  Gabor Sines ,  Omer Irshad ,  Phillippe John Louis Yu ,  Zongjie Yan ,  Ian Charles Colbert

IPC: G06F11/07 ,  G06K9/62 ,  G06N20/00

CPC classification number: G06F11/079 ,  G06F11/0775 ,  G06K9/6256 ,  G06N20/00

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.

公开(公告)号：US20230310995A1

公开(公告)日：2023-10-05

申请号：US17709904

申请日：2022-03-31

Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

Inventor： Mehdi Saeedi ,  Ian Charles Colbert ,  Thomas Daniel Perry ,  Gabor Sines

IPC: A63F13/56

CPC classification number: A63F13/56

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.

公开(公告)号：US20240095517A1

公开(公告)日：2024-03-21

申请号：US17949082

申请日：2022-09-20

Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

Inventor： Mehdi Saeedi ,  Ian Charles Colbert ,  Ihab M. A. Amer

IPC: G06N3/08

CPC classification number: G06N3/08

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.

公开(公告)号：US20240193413A1

公开(公告)日：2024-06-13

申请号：US18065393

申请日：2022-12-13

Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

Inventor： Ian Charles Colbert ,  Mehdi Saeedi ,  Arun Coimbatore Ramachandran ,  Chandra Kumar Ramasamy ,  Gabor Sines ,  Prakash Sathyanath Raghavendra ,  Alessandro Pappalardo

IPC: G06N3/08

CPC classification number: G06N3/08

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.

公开(公告)号：US12172081B2

公开(公告)日：2024-12-24

申请号：US17709904

申请日：2022-03-31

Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

Inventor： Mehdi Saeedi ,  Ian Charles Colbert ,  Thomas Daniel Perry ,  Gabor Sines

IPC: A63F13/56

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.

公开(公告)号：US20240319760A1

公开(公告)日：2024-09-26

申请号：US18126166

申请日：2023-03-24

Applicant: ADVANCED MICRO DEVICES, INC. ,  ATI TECHNOLOGIES ULC

Inventor： Ian Charles Colbert ,  Alexander Sabino Duenas ,  Stephen Jiacheng Fu ,  Omer Irshad ,  Mohammad Hamed Mousazadeh ,  Ihab Amer ,  Gabor Sines

IPC: G06F1/08 ,  G06F1/26

CPC classification number: G06F1/08 ,  G06F1/26

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.

公开(公告)号：US11994939B2

公开(公告)日：2024-05-28

申请号：US17958116

申请日：2022-09-30

Applicant: Advanced Micro Devices, Inc. ,  ATI Technologies ULC

Inventor： Mohammad Hamed Mousazadeh ,  Arpit Patel ,  Gabor Sines ,  Omer Irshad ,  Philippe John Louis Yu ,  Zongjie Yan ,  Ian Charles Colbert

IPC: G06F11/07 ,  G06N20/00

CPC classification number: G06F11/079 ,  G06F11/0706 ,  G06N20/00

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.

公开(公告)号：US20230176847A1

公开(公告)日：2023-06-08

申请号：US17545801

申请日：2021-12-08

Applicant: Advanced Micro Devices, Inc.

Inventor： Amit Ben-Moshe ,  Ian Charles Colbert

IPC: G06F8/65 ,  G06T15/00 ,  G06F8/41 ,  G06F8/51 ,  G06N20/00

CPC classification number: G06F8/65 ,  G06T15/005 ,  G06F8/443 ,  G06F8/51 ,  G06N20/00

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.

公开(公告)号：US20210065441A1

公开(公告)日：2021-03-04

申请号：US16584750

申请日：2019-09-26

Applicant: Advanced Micro Devices, Inc.

Inventor： Ian Charles Colbert ,  Michael John Bedy

IPC: G06T15/80 ,  G06N20/00 ,  G06T15/00

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.

公开(公告)号：US11868759B2

公开(公告)日：2024-01-09

申请号：US17545801

申请日：2021-12-08

Applicant: Advanced Micro Devices, Inc.

Inventor： Amit Ben-Moshe ,  Ian Charles Colbert

IPC: G06F8/65 ,  G06T15/00 ,  G06N20/00 ,  G06F8/51 ,  G06F8/41

CPC classification number: G06F8/65 ,  G06F8/443 ,  G06F8/51 ,  G06N20/00 ,  G06T15/005

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.