公开(公告)号：US12205038B2

公开(公告)日：2025-01-21

申请号：US18321691

申请日：2023-05-22

Applicant: Google LLC

Inventor： Yanqi Zhou ,  Sudip Roy ,  Amirali Abdolrashidi ,  Daniel Lin-Kit Wong ,  Chao Ma ,  Qiumin Xu ,  Azalia Mirhoseini

IPC: G06N3/04 ,  G06F18/20 ,  G06F18/21 ,  G06F18/2115 ,  G06N3/045 ,  G06N3/049 ,  G06N3/084

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.

公开(公告)号：US20230176840A1

公开(公告)日：2023-06-08

申请号：US17921933

申请日：2021-06-07

Applicant: Google LLC

Inventor： Yanqi Zhou ,  Sudip Roy ,  Amirali Abdolrashidi ,  Daniel Lin-Kit Wong ,  Chao Ma ,  Qiumin Xu ,  Hanxiao Liu ,  Phitchaya Mangpo Phothilimthana ,  Shen Wang ,  Anna Darling Goldie ,  Azalia Mirhoseini ,  James Laudon

IPC: G06F8/41

CPC classification number: G06F8/443

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for compiler optimizations using a compiler optimization network. One of the methods includes receiving an input program, wherein the input program defines a graph of operation modules, wherein each node in the graph is a respective operation module, and each edge between nodes in the graph represents one operation module receiving the output generated by another operation module. The input program is processed by a compiler optimization network comprising a graph-embedding network that is configured to encode operation features and operation dependencies of the operation modules of the input program into a graph embedding representation and a policy network that is configured to generate an optimization action for each of one or more nodes encoded in the graph embedding representation. The compiler optimization network generates an output optimization plan comprising one or more optimization actions for the input program.

公开(公告)号：US20240112027A1

公开(公告)日：2024-04-04

申请号：US18477546

申请日：2023-09-28

Applicant: Google LLC

Inventor： Yanqi Zhou ,  Yanping Huang ,  Yifeng Lu ,  Andrew M. Dai ,  Siamak Shakeri ,  Zhifeng Chen ,  James Laudon ,  Quoc V. Le ,  Da Huang ,  Nan Du ,  David Richard So ,  Daiyi Peng ,  Yingwei Cui ,  Jeffrey Adgate Dean ,  Chang Lan

IPC: G06N3/08

CPC classification number: G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing neural architecture search for machine learning models. In one aspect, a method comprises receiving training data for a machine learning, generating a plurality of candidate neural networks for performing the machine learning task, wherein each candidate neural network comprises a plurality of instances of a layer block composed of a plurality of layers, for each candidate neural network, selecting a respective type for each of the plurality of layers from a set of layer types that comprises, training the candidate neural network and evaluating performance scores for the trained candidate neural networks as applied to the machine learning task, and determining a final neural network for performing the machine learning task based at least on the performance scores for the candidate neural networks.

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：US11657289B2

公开(公告)日：2023-05-23

申请号：US16840191

申请日：2020-04-03

Applicant: Google LLC

Inventor： Yanqi Zhou ,  Sudip Roy ,  Amirali Abdolrashidi ,  Daniel Lin-Kit Wong ,  Chao Ma ,  Qiumin Xu ,  Azalia Mirhoseini

IPC: G06N3/04 ,  G06K9/62 ,  G06N3/049

CPC classification number: G06N3/0454 ,  G06K9/6231 ,  G06K9/6262 ,  G06K9/6296 ,  G06N3/049

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.

公开(公告)号：US20250131251A1

公开(公告)日：2025-04-24

申请号：US18834070

申请日：2023-01-30

Applicant: Google LLC

Inventor： Hanxiao Liu ,  Quoc V. Le ,  Yanqi Zhou ,  Tao Lei ,  Yuzhe Zhao ,  Yanping Huang ,  Nan Du ,  Zhifeng Chen ,  Andrew M. Dai ,  James Laudon

IPC: G06N3/048

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing a machine learning task on a network input to generate a network output. In one aspect, one of the systems includes a neural network configured to perform the machine learning task, the neural network including one or more expert neural network blocks that each include router that performs expert-choice routing between multiple expert neural networks.

公开(公告)号：US20240403660A1

公开(公告)日：2024-12-05

申请号：US18697182

申请日：2022-10-06

Applicant: Google LLC

Inventor： Xinfeng Xie ,  Azalia Mirhoseini ,  James Laudon ,  Phitchaya Mangpo Phothilimthana ,  Sudip Roy ,  Prakash Janardhana Prabhu ,  Ulysse Beaugnon ,  Yanqi Zhou

IPC: G06N5/01 ,  G06N3/042 ,  G06N3/045 ,  G06N3/092

Abstract: Systems and methods for determining a placement for computational graph across multiple hardware devices. One of the methods includes generating a policy output using a policy neural network and using the policy output to generate a final placement that satisfies one or more constraints.

公开(公告)号：US20240303464A1

公开(公告)日：2024-09-12

申请号：US18598876

申请日：2024-03-07

Applicant: Google LLC

Inventor： Nan Du ,  Tao Wang ,  Yanqi Zhou ,  Tao Lei ,  Yuanzhong Xu ,  Andrew Mingbo Dai ,  Zhifeng Chen ,  Dewen Zeng ,  Yingwei Cui

IPC: G06N3/04 ,  G06N3/084

CPC classification number: G06N3/04 ,  G06N3/084

Abstract: A method includes providing a first set of data objects to a first skip router of a neural network (NN). The NN includes a first NN layer and a second NN layer. The first set of data objects is subdivided into a first set of skip objects and a first set of non-skip objects based on a first skip logic implemented by the first skip router and a first context of each data object in the first set of data objects. A first set of processed objects is generated based on the first set of non-skip objects and a first layer logic implemented by the first NN layer. Predictions are generated based on a second set of data objects and a second layer logic implemented by the second NN layer. The second set of data objects includes the first set of processed objects and the first set of skip objects.

公开(公告)号：US20230306266A1

公开(公告)日：2023-09-28

申请号：US18321691

申请日：2023-05-22

Applicant: Google LLC

Inventor： Yanqi Zhou ,  Sudip Roy ,  Amirali Abdolrashidi ,  Daniel Lin-Kit Wong ,  Chao Ma ,  Qiumin Xu ,  Azalia Mirhoseini

IPC: G06N3/084 ,  G06N3/049 ,  G06F18/20 ,  G06F18/21 ,  G06F18/2115 ,  G06N3/045

CPC classification number: G06N3/084 ,  G06N3/049 ,  G06F18/29 ,  G06F18/217 ,  G06F18/2115 ,  G06N3/045

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.

公开(公告)号：US20210248445A1

公开(公告)日：2021-08-12

申请号：US16840191

申请日：2020-04-03

Applicant: Google LLC

Inventor： Yanqi Zhou ,  Sudip Roy ,  Amirali Abdolrashidi ,  Daniel Lin-Kit Wong ,  Chao Ma ,  Qiumin Xu ,  Azalia Mirhoseini

IPC: G06N3/04 ,  G06K9/62

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for optimizing the execution of the operations of a neural network. One of the methods includes obtaining data representing a graph characterizing a plurality of operations of a neural network, wherein each node of the graph characterizes an operation of the neural network and each edge of the graph characterizes data dependency between the operations; processing the data representing the graph using a graph embedding neural network to generate an embedding of the graph; and processing the embedding of the graph using a policy neural network to generate a task output, wherein the task output comprises, for each of the plurality of operations of the neural network, a respective decision for a particular optimization task.