公开(公告)号：US20230117786A1

公开(公告)日：2023-04-20

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

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：US20220405493A1

公开(公告)日：2022-12-22

申请号：US17842288

申请日：2022-06-16

Applicant: Google LLC

Inventor： Anna Darling Goldie

IPC: G06F40/58 ,  G06F40/216 ,  G06F40/284 ,  G06K9/62

Abstract: Example aspects of the present disclosure are directed to systems and methods for generation of improved language embeddings (e.g., entity embeddings for natural language tokens) which provide improved model performance. In addition, the proposed techniques require less computational consumption relative to previous approaches.

公开(公告)号：US20220383036A1

公开(公告)日：2022-12-01

申请号：US17764015

申请日：2020-09-25

Applicant: Google LLC

Inventor： Azade Nazi ,  Azalia Mirhoseini ,  Anna Darling Goldie ,  Sujith Ravi ,  William Hang

IPC: G06K9/62 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a clustering neural network. One of the methods includes obtaining unlabeled training data; and training the clustering neural network on the unlabeled training data to determine trained values of the clustering parameters by minimizing a normalized cuts loss function that includes a first term that measures an expected normalized cuts of clustering nodes in a graph representing the data set into the plurality of clusters according to clustering outputs generated by the clustering neural network.

公开(公告)号：US11455514B2

公开(公告)日：2022-09-27

申请号：US16554217

申请日：2019-08-28

Applicant: Google LLC

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

IPC: G06N3/04 ,  G06F9/50 ,  G06N20/00 ,  G06F16/901 ,  G06N3/063 ,  G06N3/08 ,  G06N5/04

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：US11216609B2

公开(公告)日：2022-01-04

申请号：US17238128

申请日：2021-04-22

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 ,  G06N3/08 ,  G06F30/398

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：US10699043B2

公开(公告)日：2020-06-30

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: G06F17/50 ,  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.