Google Inc.

Ebrahim Songhori

Azalia Mirhoseini

James Laudon

Anna Darling Goldie

Wenjie Jiang

Shen Wang

Roger David Carpenter

Young-Joon Lee

Jeffrey Adgate Dean

Quoc V Le

Chian-Min Richard Ho

Mustafa Nazim Yazgan

Kavya Srinivasa Setty

Omkar Pathak

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.

Alignment cost for integrated circuit placement

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.

Generating integrated circuit placements using neural networks

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San Jose, CA, United States of America

Ebrahim Songhori