Generating integrated circuit placements using neural networks

Patent metadata

Field	Value
Publication number	US-2023117786-A1
Application number	US-202218082392-A
Country	US
Kind code	A1
Filing date	Dec 15, 2022
Priority date	Apr 22, 2020
Publication date	Apr 20, 2023
Grant date	—

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.

First claim

What is claimed is:

1 . A method of training a node placement neural network that comprises:
an encoder 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 an encoder output, and a policy neural network configured to, at each of the plurality of time steps, receive an encoded representation generated from the encoder output generated by the encoder neural network and process the encoded representation to generate a score distribution over a plurality of positions on the surface of the integrated circuit chip, the method comprising: obtaining supervised training data comprising:
a plurality of training input representations, each training input representation representing a respective placement of a respective netlist of nodes, and
for each training input representation, a respective target value of a reward function that measures a quality of the placement of the respective netlist of nodes; and
training the encoder neural network jointly with a reward prediction neural network on the supervised training data through supervised learning, wherein the reward prediction neural network is configured to, for each training encoder input, receive the encoder output generated by the encoder neural network from the training input representation and process the encoded representation to generate a predicted value of the reward function for the placement represented by the training input representation.

Assignees

• Google Llc

Inventors

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

Classifications

• G06N3/0455

  Auto-encoder networks; Encoder-decoder networks · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/092

  Reinforcement learning · CPC title

• G06N3/098

  Distributed learning, e.g. federated learning · CPC title

• G06N3/0442

  characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU] · CPC title