公开(公告)号：US20240273270A1

公开(公告)日：2024-08-15

申请号：US18564797

申请日：2022-05-31

Applicant: Google LLC

Inventor： Shobha Vasudevan ,  Wenjie Jiang ,  Charles Aloysius Sutton ,  Rishabh Singh ,  David Bieber ,  Milad Olia Hashemi ,  Chian-min Richard Ho ,  Hamid Shojaei

IPC: G06F30/323 ,  G06F30/33

CPC classification number: G06F30/323 ,  G06F30/33

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating learned representations of digital circuit designs. One of the systems includes obtaining data representing a program that implements a digital circuit design, the program comprising a plurality of statements; processing the obtained data to generate data representing a graph representing the digital circuit design, the graph comprising: a plurality of nodes representing respective statements of the program, a plurality of first edges each representing a control flow between a pair of statements of the program, and a plurality of second edges each representing a data flow between a pair of statements of the program; and generating a learned representation of the digital circuit design, comprising processing the data representing the graph using a graph neural network to generate a respective learned representation of each statement represented by a node of the graph.

公开(公告)号：US20220376984A1

公开(公告)日：2022-11-24

申请号：US17323464

申请日：2021-05-18

Applicant: Google LLC

Inventor： Min Wen ,  Wenjie Jiang ,  Anurag Sharma ,  Matthew Johnston ,  Rodolfo Enrique Alvizu Gomez

IPC: H04L12/24 ,  H04L12/751

Abstract: Methods, systems, and apparatus, including computer-readable storage media, optimizing interior gateway protocol (IGP) metrics using reinforcement learning (RL) for a network domain. The system can receive a topology (G) of a network domain, a set of flows (F), and an objective function. The system can optimize, using reinforcement learning, the objective function based on the received topology and the one or more flows F. The system can determine updated IGP metrics based on the optimization of the objective function. The IGP metrics for the metric domain may be updated with the updated IGP metrics.

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：US11811614B2

公开(公告)日：2023-11-07

申请号：US18175127

申请日：2023-02-27

Applicant: Google LLC

Inventor： Min Wen ,  Wenjie Jiang ,  Anurag Sharma ,  Matthew Johnston ,  Rodolfo Enrique Alvizu Gomez

IPC: H04L12/28 ,  H04L41/12 ,  H04L41/14 ,  H04L45/02

CPC classification number: H04L41/12 ,  H04L41/14 ,  H04L45/02 ,  H04L45/08

Abstract: Methods, systems, and apparatus, including computer-readable storage media, optimizing interior gateway protocol (IGP) metrics using reinforcement learning (RL) for a network domain. The system can receive a topology (G) of a network domain, a set of flows (F), and an objective function. The system can optimize, using reinforcement learning, the objective function based on the received topology and the one or more flows F. The system can determine updated IGP metrics based on the optimization of the objective function. The IGP metrics for the metric domain may be updated with the updated IGP metrics.

公开(公告)号：US20250124207A1

公开(公告)日：2025-04-17

申请号：US18570915

申请日：2022-12-15

Applicant: Google LLC

Inventor： Ebrahim Songhori ,  Wenjie Jiang ,  Sergio Guadarrama Cotado ,  Young-Joon Lee ,  Azalia Mirhoseini ,  Anna Darling Goldie ,  Roger David Carpenter ,  Yuting Yue ,  Kuang-Huei Lee ,  James Laudon ,  Toby James Boyd ,  Quoc V. Le

IPC: G06F30/392 ,  G06F30/27 ,  G06F30/394

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating a computer chip placement. One of the methods includes training, through reinforcement learning, a node placement neural network that is configured to, at each of a plurality of time steps, receive an input representation comprising data representing a current state of a placement of a netlist of nodes on a surface of an integrated circuit chip as of the time step and process the input representation to generate a score distribution over a plurality of positions on the surface of the integrated circuit chip.

公开(公告)号：US20230208720A1

公开(公告)日：2023-06-29

申请号：US18175127

申请日：2023-02-27

Applicant: Google LLC

Inventor： Min Wen ,  Wenjie Jiang ,  Anurag Sharma ,  Matthew Johnston ,  Rodolfo Enrique Alvizu Gomez

IPC: H04L41/12 ,  H04L41/14 ,  H04L45/02

CPC classification number: H04L41/12 ,  H04L41/14 ,  H04L45/02 ,  H04L45/08

Abstract: Methods, systems, and apparatus, including computer-readable storage media, optimizing interior gateway protocol (IGP) metrics using reinforcement learning (RL) for a network domain. The system can receive a topology (G) of a network domain, a set of flows (F), and an objective function. The system can optimize, using reinforcement learning, the objective function based on the received topology and the one or more flows F. The system can determine updated IGP metrics based on the optimization of the objective function. The IGP metrics for the metric domain may be updated with the updated IGP metrics.

公开(公告)号：US20210334445A1

公开(公告)日：2021-10-28

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 ,  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：US20240095424A1

公开(公告)日：2024-03-21

申请号：US17890370

申请日：2022-08-18

Applicant: Google LLC

Inventor： Ebrahim Mohammadgholi Songhori ,  Shen Wang ,  Azalia Mirhoseini ,  Anna Goldie ,  Roger Carpenter ,  Wenjie Jiang ,  Young-Joon Lee ,  James Laudon

IPC: G06F30/27 ,  G06F30/392

CPC classification number: G06F30/27 ,  G06F30/392

Abstract: Aspects of the disclosure are directed to automatically determining floor planning in chips, which factors in memory macro alignment. A deep reinforcement learning (RL) agent can be trained to determine optimal placements for the memory macros, where memory macro alignment can be included as a regularization cost to be added to the placement objective as a RL reward. Tradeoffs between the placement objective and alignment of macros can be controlled by a tunable alignment parameter.

公开(公告)号：US11601336B2

公开(公告)日：2023-03-07

申请号：US17323464

申请日：2021-05-18

Applicant: Google LLC

Inventor： Min Wen ,  Wenjie Jiang ,  Anurag Sharma ,  Matthew Johnston ,  Rodolfo Enrique Alvizu Gomez

IPC: H04L12/28 ,  H04L41/12 ,  H04L45/02 ,  H04L41/14

Abstract: Methods, systems, and apparatus, including computer-readable storage media, optimizing interior gateway protocol (IGP) metrics using reinforcement learning (RL) for a network domain. The system can receive a topology (G) of a network domain, a set of flows (F), and an objective function. The system can optimize, using reinforcement learning, the objective function based on the received topology and the one or more flows F. The system can determine updated IGP metrics based on the optimization of the objective function. The IGP metrics for the metric domain may be updated with the updated IGP metrics.

公开(公告)号：US11556690B2

公开(公告)日：2023-01-17

申请号：US17555085

申请日：2021-12-17

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: G06F30/39 ,  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.