公开(公告)号：US20210319157A1

公开(公告)日：2021-10-14

申请号：US17225946

申请日：2021-04-08

Applicant: Google LLC

Inventor： Amir Yazdanbakhsh

IPC: G06F30/27 ,  G06F30/337 ,  G06N3/04 ,  G06F8/41

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing integrated circuit architectures or compiler designs using an optimization engine. The optimization engine includes an auto-encoder and one or more regressors. Once trained, the optimization engine can encode initial, discrete input values of a set of input characteristics into a continuous domain and use continuous optimization techniques to identify final input values of the set of input characteristics that optimize one or more output characteristics.

公开(公告)号：US20240005129A1

公开(公告)日：2024-01-04

申请号：US18029849

申请日：2021-10-01

Applicant: Google LLC

Inventor： Yanqi Zhou ,  Amir Yazdanbakhsh ,  Berkin Akin ,  Daiyi Peng ,  Yuxiong Zhu ,  Mingxing Tan ,  Xuanyi Dong

IPC: G06N3/045 ,  G06N3/092 ,  G06N3/063 ,  G06N3/044 ,  G06N3/0464

CPC classification number: G06N3/045 ,  G06N3/092 ,  G06N3/0464 ,  G06N3/044 ,  G06N3/063

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for jointly determining neural network architectures and hardware accelerator architectures. In one aspect, a method includes: generating, using a controller policy, a batch of one or more output sequences, each output sequence in the batch defining a respective architecture of a child neural network and a respective architecture of a hardware accelerator; for each output sequence in the batch: training a respective instance of the child neural network having the architecture defined by the output sequence; evaluating a network performance of the trained instance of the child neural; and evaluating an accelerator performance of a respective instance of the hardware accelerator having the architecture defined by the output sequence to determine an accelerator performance metric for the instance of the hardware accelerator; and using the network performance metrics and the accelerator performance metrics to adjust the controller policy.

公开(公告)号：US11556684B2

公开(公告)日：2023-01-17

申请号：US17225946

申请日：2021-04-08

Applicant: Google LLC

Inventor： Amir Yazdanbakhsh

IPC: G06F8/30 ,  G06F30/27 ,  G06F8/41 ,  G06N3/04 ,  G06F30/337

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing integrated circuit architectures or compiler designs using an optimization engine. The optimization engine includes an auto-encoder and one or more regressors. Once trained, the optimization engine can encode initial, discrete input values of a set of input characteristics into a continuous domain and use continuous optimization techniques to identify final input values of the set of input characteristics that optimize one or more output characteristics.

公开(公告)号：US11960805B2

公开(公告)日：2024-04-16

申请号：US18066900

申请日：2022-12-15

Applicant: Google LLC

Inventor： Amir Yazdanbakhsh

IPC: G06F8/30 ,  G06F8/41 ,  G06F30/27 ,  G06F30/337 ,  G06N3/045

CPC classification number: G06F30/27 ,  G06F8/41 ,  G06F30/337 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing integrated circuit architectures or compiler designs using an optimization engine. The optimization engine includes an auto-encoder and one or more regressors. Once trained, the optimization engine can encode initial, discrete input values of a set of input characteristics into a continuous domain and use continuous optimization techniques to identify final input values of the set of input characteristics that optimize one or more output characteristics.

公开(公告)号：US20230229929A1

公开(公告)日：2023-07-20

申请号：US18011630

申请日：2021-01-28

Applicant: Google LLC

Inventor： Amir Yazdanbakhsh ,  Yu Zheng ,  Junchao Chen

IPC: G06N3/092 ,  G06N3/098

CPC classification number: G06N3/092 ,  G06N3/098

Abstract: A computing system for performing distributed large scale reinforcement learning with improved efficiency can include a plurality of actor devices, wherein each actor device locally stores a local version of a machine-learned model, wherein each actor device is configured to implement the local version of the machine-learned model at the actor device to determine an action to take in an environment to generate an experience, a server computing system configured to perform one or more learning algorithms to learn an updated version of the machine-learned model based on the experiences generated by the plurality of actor devices, and a hierarchical and distributed data caching system including a plurality of layers of data caches that propagate data descriptive of the updated version of the machine-learned model from the server computing system to the plurality of actor devices to enable each actor device to update its respective local version of the model.

公开(公告)号：US20250078815A1

公开(公告)日：2025-03-06

申请号：US18826135

申请日：2024-09-05

Applicant: Google LLC

Inventor： Shaojin Ding ,  David Qiu ,  David Rim ,  Amir Yazdanbakhsh ,  Yanzhang He ,  Zhonglin Han ,  Rohit Prakash Prabhavalkar ,  Weiran Wang ,  Bo Li ,  Jian Li ,  Tara N. Sainath ,  Shivani Agrawal ,  Oleg Rybakov

IPC: G10L15/06

Abstract: A method includes obtaining a plurality of training samples that each include a respective speech utterance and a respective textual utterance representing a transcription of the respective speech utterance. The method also includes fine-tuning, using quantization and sparsity aware training with native integer operations, a pre-trained automatic speech recognition (ASR) model on the plurality of training samples. Here, the pre-trained ASR model includes a plurality of weights and the fine-tuning includes pruning one or more weights of the plurality of weights using a sparsity mask and quantizing each weight of the plurality of weights based on an integer with a fixed-bit width. The method also includes providing the fine-tuned ASR model to a user device.

公开(公告)号：US20240311267A1

公开(公告)日：2024-09-19

申请号：US18575621

申请日：2022-06-30

Applicant: Google LLC

Inventor： Amir Yazdanbakhsh ,  Sergey Vladimir Levine ,  Aviral Kumar

IPC: G06F11/34 ,  G06F11/30

CPC classification number: G06F11/3447 ,  G06F11/3024

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a surrogate neural network configured to determine a predicted performance measure of a hardware accelerator having a target hardware configuration on a target application. The trained instance of the surrogate neural network can be used. in addition to or in place of hardware simulation, during a search process for determining hardware configurations for application-specific hardware accelerators. i.e., hardware accelerators on which one or more neural networks can be deployed to perform one or more target machine learning tasks.

公开(公告)号：US20230123343A1

公开(公告)日：2023-04-20

申请号：US18066900

申请日：2022-12-15

Applicant: Google LLC

Inventor： Amir Yazdanbakhsh

IPC: G06F30/27 ,  G06F8/41 ,  G06F30/337 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for optimizing integrated circuit architectures or compiler designs using an optimization engine. The optimization engine includes an auto-encoder and one or more regressors. Once trained, the optimization engine can encode initial, discrete input values of a set of input characteristics into a continuous domain and use continuous optimization techniques to identify final input values of the set of input characteristics that optimize one or more output characteristics.