公开(公告)号：US20200380023A1

公开(公告)日：2020-12-03

申请号：US16998891

申请日：2020-08-20

Applicant: Google LLC

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

IPC: 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：US20200175216A1

公开(公告)日：2020-06-04

申请号：US16703837

申请日：2019-12-04

Applicant: Google LLC

Inventor： Chian-min Richard Ho ,  William Hang ,  Mustafa Nazim Yazgan ,  Anna Darling Goldie ,  Jeffrey Adgate Dean ,  Azalia Mirhoseini ,  Emre Tuncer ,  Ya Wang ,  Anand Babu

IPC: G06F30/27 ,  G06F30/392

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective 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 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：US10650328B2

公开(公告)日：2020-05-12

申请号：US16368526

申请日：2019-03-28

Applicant: Google LLC

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

IPC: G06N20/00 ,  G06N7/00 ,  G06N3/04 ,  G06N20/20 ,  G06N3/08

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：US10402721B2

公开(公告)日：2019-09-03

申请号：US15595644

申请日：2017-05-15

Applicant: Google LLC

Inventor： Gregory Sean Corrado ,  Jeffrey Adgate Dean

IPC: G06N3/02 ,  G06N3/04 ,  G06N3/08 ,  G06N3/10 ,  G16H50/20 ,  G16H50/30

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using recurrent neural networks to analyze health events. One of the methods includes: processing each of a plurality of initial temporal sequences of health events to generate, for each of the initial temporal sequences, a respective network internal state of a recurrent neural network for each time step in the initial temporal sequence; storing, for each of the initial temporal sequences, one or more of the network internal states for the time steps in the temporal sequence in a repository; obtaining a first temporal sequence; processing the first temporal sequence using the recurrent neural network to generate a sequence internal state for the first temporal sequence; and selecting one or more initial temporal sequences that are likely to include health events that are predictive of future health events in the first temporal sequence.

公开(公告)号：US12056089B2

公开(公告)日：2024-08-06

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 ,  G06F16/174 ,  G06F16/182 ,  G06F16/215

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

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.

公开(公告)号：US11803747B2

公开(公告)日：2023-10-31

申请号：US16878720

申请日：2020-05-20

Applicant: Google LLC

Inventor： Samuel Bengio ,  Mohammad Norouzi ,  Benoit Steiner ,  Jeffrey Adgate Dean ,  Hieu Hy Pham ,  Azalia Mirhoseini ,  Quoc V. Le ,  Naveen Kumar ,  Yuefeng Zhou ,  Rasmus Munk Larsen

IPC: G06N3/08 ,  G06N5/04 ,  G06N3/10 ,  G06N3/044 ,  G06N3/045

CPC classification number: G06N3/08 ,  G06N3/044 ,  G06N3/045 ,  G06N3/105 ,  G06N5/04

Abstract: A method for determining a placement for machine learning model operations across multiple hardware devices is described. The method includes receiving data specifying a machine learning model to be placed for distributed processing on multiple hardware devices; generating, from the data, a sequence of operation embeddings, each operation embedding in the sequence characterizing respective operations necessary to perform the processing of the machine learning model; processing the sequence of operation embeddings using a placement recurrent neural network in accordance with first values of a plurality network parameters of the placement recurrent neural network to generate a network output that defines a placement of the operations characterized by the operation embeddings in the sequence across the plurality of devices; and scheduling the machine learning model for processing by the multiple hardware devices by placing the operations on the multiple devices according to the placement defined by the network output.

公开(公告)号：US11675940B2

公开(公告)日：2023-06-13

申请号：US17409566

申请日：2021-08-23

Applicant: Google LLC

Inventor： Chian-Min Richard Ho ,  William Hang ,  Mustafa Nazim Yazgan ,  Anna Darling Goldie ,  Jeffrey Adgate Dean ,  Azalia Mirhoseini ,  Emre Tuncer ,  Ya Wang ,  Anand Babu

IPC: G06F30/27 ,  G06F30/392

CPC classification number: G06F30/27 ,  G06F30/392

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip floorplan. One of the methods includes obtaining netlist data for a computer chip; and generating a computer chip floorplan, comprising placing a respective 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 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：US11556381B2

公开(公告)日：2023-01-17

申请号：US17738909

申请日：2022-05-06

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: G06F15/82 ,  G06F9/48 ,  G06N3/08 ,  G06N3/063

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：US20220351091A1

公开(公告)日：2022-11-03

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 ,  G06N3/04 ,  G06N3/08

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：US20220019896A1

公开(公告)日：2022-01-20

申请号：US17392690

申请日：2021-08-03

Applicant: Google LLC

Inventor： Vijay Vasudevan ,  Jeffrey Adgate Dean ,  Sanjay Ghemawat

IPC: G06N3/08 ,  G06F9/50 ,  G06F9/54 ,  G06N3/063 ,  G06N3/04 ,  G06F17/16

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for modifying a computational graph to include send and receive nodes. Communication between unique devices performing operations of different subgraphs of the computational graph can be handled efficiently by inserting send and receive nodes into each subgraph. When executed, the operations that these send and receive nodes represent may enable pairs of unique devices to conduct communication with each other in a self-sufficient manner. This shifts the burden of coordinating communication away from the backend, which affords the system that processes this computational graph representation the opportunity to perform one or more other processes while devices are executing subgraphs.