公开(公告)号：US11657289B2

公开(公告)日：2023-05-23

申请号：US16840191

申请日：2020-04-03

申请人： Google LLC

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

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

CPC分类号： G06N3/0454 ,  G06K9/6231 ,  G06K9/6262 ,  G06K9/6296 ,  G06N3/049

摘要： 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

申请人： Google LLC

发明人： 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分类号： G06F8/41

CPC分类号： G06F8/443

摘要： 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：US20230306266A1

公开(公告)日：2023-09-28

申请号：US18321691

申请日：2023-05-22

申请人： Google LLC

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

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

CPC分类号： G06N3/084 ,  G06N3/049 ,  G06F18/29 ,  G06F18/217 ,  G06F18/2115 ,  G06N3/045

摘要： 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

申请人： Google LLC

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

IPC分类号： G06N3/04 ,  G06K9/62

摘要： 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.