公开(公告)号：US10332160B2

公开(公告)日：2019-06-25

申请号：US15593862

申请日：2017-05-12

Applicant: Google LLC

Inventor： Jeffrey Adgate Dean ,  Krishna Bharat ,  Paul Buchheit

IPC: G06F17/24 ,  G06Q30/02 ,  G06F16/951 ,  G06F16/9535

Abstract: The usefulness of content (target content), such as advertisements, may be increased by determining additional content and providing such additional content in association with the content. The target content may be text, a Web page, a URL, a search query, etc. The additional content might be related suggested queries (e.g. “Try a search for ——————”), news articles (or excerpts or summaries thereof), reviews (or excerpts or summaries thereof), advertisements, user group messages, etc.

公开(公告)号：US20190026624A1

公开(公告)日：2019-01-24

申请号：US16040186

申请日：2018-07-19

Applicant: Google LLC

Inventor： Benoit Steiner ,  Anna Darling Goldie ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le

IPC: G06N3/04 ,  G06N3/08 ,  G06N5/04 ,  G06N99/00 ,  G06F17/30 ,  G06N3/063

CPC classification number: G06N3/0454 ,  G06F9/5066 ,  G06F16/9024 ,  G06N3/0445 ,  G06N3/063 ,  G06N3/084 ,  G06N5/045 ,  G06N20/00

Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices includes receiving data specifying machine learning operations, and determining a placement that assigns each of the operations specified by the data to a respective device from the multiple hardware devices. Determining the placement includes: generating, from the data, a respective operation embedding for each of the operations; grouping the operations into multiple operation groups, comprising processing each of the respective operation embeddings using a grouper neural network having multiple grouper parameters, in which the grouper neural network is configured to, for each of the operations, process the operation embedding for the operation in accordance with first values of the grouper parameters to generate a grouper output that assigns the operation to an operation group from the multiple operation groups; and assigning each of the operation groups to a respective device from the multiple hardware devices.

公开(公告)号：US20180341983A1

公开(公告)日：2018-11-29

申请号：US16055004

申请日：2018-08-03

Applicant: GOOGLE LLC

Inventor： Jeffrey Adgate Dean ,  Georges Harik ,  Paul Buchheit

IPC: G06Q30/02 ,  G06F17/30

CPC classification number: G06Q30/0256 ,  G06F16/24565 ,  G06Q30/02 ,  G06Q30/0254 ,  G06Q30/0261 ,  G06Q30/0269 ,  G06Q30/0277

Abstract: Targeting information (also referred to as ad “serving constraints”) or candidate targeting information for an advertisement is identified. Targeting information may be identified by extracting topics or concepts from, and/or generating topics or concepts based on, ad information, such as information from a Web page to which an ad is linked (or some other Web page of interest to the ad or advertiser). The topics or concepts may be relevant queries associated with the Web page of interest, clusters, etc.

公开(公告)号：US20180247197A1

公开(公告)日：2018-08-30

申请号：US15965742

申请日：2018-04-27

Applicant: Google LLC

Inventor： Paul A. Tucker ,  Jeffrey Adgate Dean ,  Sanjay Ghemawat ,  Yuan Yu

IPC: G06N3/08 ,  G06N5/04

CPC classification number: G06N3/08 ,  G06F9/5038 ,  G06F9/5066 ,  G06N3/0454 ,  G06N3/063 ,  G06N3/084 ,  G06N5/048

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving a request from a client to process a computational graph; obtaining data representing the computational graph, the computational graph comprising a plurality of nodes and directed edges, wherein each node represents a respective operation, wherein each directed edge connects a respective first node to a respective second node that represents an operation that receives, as input, an output of an operation represented by the respective first node; identifying a plurality of available devices for performing the requested operation; partitioning the computational graph into a plurality of subgraphs, each subgraph comprising one or more nodes in the computational graph; and assigning, for each subgraph, the operations represented by the one or more nodes in the subgraph to a respective available device in the plurality of available devices for operation.

公开(公告)号：US12112198B2

公开(公告)日：2024-10-08

申请号：US18082415

申请日：2022-12-15

Applicant: Google LLC

Inventor： Jeffrey Adgate Dean ,  Sudip Roy ,  Michael Acheson Isard ,  Aakanksha Chowdhery ,  Brennan Saeta ,  Chandramohan Amyangot Thekkath ,  Daniel William Hurt ,  Hyeontaek Lim ,  Laurent El Shafey ,  Parker Edward Schuh ,  Paul Ronald Barham ,  Ruoming Pang ,  Ryan Sepassi ,  Sanjay Ghemawat ,  Yonghui Wu

IPC: G06F17/10 ,  G06F9/48 ,  G06N3/063 ,  G06N3/08

CPC classification number: G06F9/4881 ,  G06N3/063 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for distributing machine learning workloads, e.g., computations for training a neural network or computing an inference using a neural network, across multiple hardware accelerators. One of the systems comprises a plurality of accelerator islands, each hardware accelerator island comprising a respective plurality of hardware devices that include a plurality of hardware accelerators and a corresponding host for each of the plurality of hardware accelerators; and a respective scheduler for each of the accelerator islands that is configured to schedule workloads across the plurality of accelerators and corresponding hosts in the accelerator island, wherein the system is configured to: receive data representing a machine learning workload; and assign a respective portion of the machine learning workload to each of the plurality of accelerator islands for scheduling by the respective scheduler for the accelerator island.

公开(公告)号：US20240220527A1

公开(公告)日：2024-07-04

申请号：US18606458

申请日：2024-03-15

Applicant: Google LLC

Inventor： Gregory Sean Corrado ,  Tomas Mikolov ,  Samuel Bengio ,  Yoram Singer ,  Jonathon Shlens ,  Andrea L. Frome ,  Jeffrey Adgate Dean ,  Mohammad Norouzi

IPC: G06F16/35 ,  G06F16/50

CPC classification number: G06F16/35 ,  G06F16/50

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for classifying data objects. One of the methods includes obtaining data that associates each term in a vocabulary of terms with a respective high-dimensional representation of the term; obtaining classification data for a data object, wherein the classification data includes a respective score for each of a plurality of categories, and wherein each of the categories is associated with a respective category label; computing an aggregate high-dimensional representation for the data object from high-dimensional representations for the category labels associated with the categories and the respective scores; identifying a first term in the vocabulary of terms having a high-dimensional representation that is closest to the aggregate high-dimensional representation; and selecting the first term as a category label for the data object.

公开(公告)号：US20240144109A1

公开(公告)日：2024-05-02

申请号：US18399358

申请日：2023-12-28

Applicant: Google LLC

Inventor： Oriol Vinyals ,  Jeffrey Adgate Dean ,  Geoffrey E. Hinton

IPC: G06N20/20 ,  G06N3/045 ,  G06N3/084 ,  G06N7/00 ,  G06N7/01 ,  G06N20/00

CPC classification number: G06N20/20 ,  G06N3/045 ,  G06N3/084 ,  G06N7/00 ,  G06N7/01 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a distilled machine learning model. One of the methods includes training a cumbersome machine learning model, wherein the cumbersome machine learning model is configured to receive an input and generate a respective score for each of a plurality of classes; and training a distilled machine learning model on a plurality of training inputs, wherein the distilled machine learning model is also configured to receive inputs and generate scores for the plurality of classes, comprising: processing each training input using the cumbersome machine learning model to generate a cumbersome target soft output for the training input; and training the distilled machine learning model to, for each of the training inputs, generate a soft output that matches the cumbersome target soft output for the training input.

公开(公告)号：US11900232B2

公开(公告)日：2024-02-13

申请号：US17863733

申请日：2022-07-13

Applicant: Google LLC

Inventor： Oriol Vinyals ,  Jeffrey Adgate Dean ,  Geoffrey E. Hinton

IPC: G06N20/20 ,  G06N7/00 ,  G06N20/00 ,  G06N3/084 ,  G06N3/045 ,  G06N7/01

CPC classification number: G06N20/20 ,  G06N3/045 ,  G06N3/084 ,  G06N7/00 ,  G06N7/01 ,  G06N20/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a distilled machine learning model. One of the methods includes training a cumbersome machine learning model, wherein the cumbersome machine learning model is configured to receive an input and generate a respective score for each of a plurality of classes; and training a distilled machine learning model on a plurality of training inputs, wherein the distilled machine learning model is also configured to receive inputs and generate scores for the plurality of classes, comprising: processing each training input using the cumbersome machine learning model to generate a cumbersome target soft output for the training input; and training the distilled machine learning model to, for each of the training inputs, generate a soft output that matches the cumbersome target soft output for the training input.

公开(公告)号：US11853677B2

公开(公告)日：2023-12-26

申请号：US18082392

申请日：2022-12-15

Applicant: Google LLC

Inventor： Anna Darling Goldie ,  Azalia Mirhoseini ,  Ebrahim Songhori ,  Wenjie Jiang ,  Shen Wang ,  Roger David Carpenter ,  Young-Joon Lee ,  Mustafa Nazim Yazgan ,  Chian-min Richard Ho ,  Quoc V. Le ,  James Laudon ,  Jeffrey Adgate Dean ,  Kavya Srinivasa Setty ,  Omkar Pathak

IPC: G06F30/392 ,  G06F30/398 ,  G06N3/08

CPC classification number: G06F30/392 ,  G06F30/398 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip placement, comprising placing a respective macro node at each time step in a sequence comprising a plurality of time steps, the placing comprising, for each time step: generating an input representation for the time step; processing the input representation using a node placement neural network having a plurality of network parameters, wherein the node placement neural network is configured to process the input representation in accordance with current values of the network parameters to generate a score distribution over a plurality of positions on the surface of the computer chip; and assigning the macro node to be placed at the time step to a position from the plurality of positions using the score distribution.

公开(公告)号：US20230409527A1

公开(公告)日：2023-12-21

申请号：US18239475

申请日：2023-08-29

Applicant: Google LLC

Inventor： Yasushi Saito ,  Sanjay Ghemawat ,  Jeffrey Adgate Dean

IPC: G06F16/16 ,  G06F16/11 ,  G06F16/182 ,  G06F16/215 ,  G06F16/174

CPC classification number: G06F16/162 ,  G06F16/11 ,  G06F16/182 ,  G06F16/215 ,  G06F16/1748

Abstract: A method for deleting obsolete files from a file system is provided. The method includes receiving a request to delete a reference to a first target file of a plurality of target files stored in a file system, the first target file having a first target file name. A first reference file whose file name includes the first target file name is identified. The first reference file is deleted from the file system. The method further includes determining whether the file system includes at least one reference file, distinct from the first reference file, whose file name includes the first target file name. In accordance with a determination that the file system does not include the at least one reference file, the first target file is deleted from the file system.