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DREAM-GAN: Advancing DREAMPlace towards Commercial-Quality using Generative Adversarial Learning

Authors:

Hao-Hsiang Hsiao,

Sung Kyu LimAuthors Info & Claims

ISPD '23: Proceedings of the 2023 International Symposium on Physical Design

Pages 141 - 148

https://doi.org/10.1145/3569052.3572993

Published: 26 March 2023 Publication History

Abstract

DREAMPlace is a renowned open-source placer that provides GPU-acceleratable infrastructure for placements of Very-Large-Scale-Integration (VLSI) circuits. However, due to its limited focus on wirelength and density, existing placement solutions of DREAMPlace are not applicable to industrial design flows. To improve DREAMPlace towards commercial-quality without knowing the black-boxed algorithms of the tools, in this paper, we present DREAM-GAN, a placement optimization framework that advances DREAMPlace using generative adversarial learning. At each placement iteration, aside from optimizing the wirelength and density objectives of the vanilla DREAMPlace, DREAM-GAN computes and optimizes a differentiable loss that denotes the similarity score between the underlying placement and the tool-generated placements in commercial databases. Experimental results on 5 commercial and OpenCore designs using an industrial design flow implemented by Synopsys ICC2 not only demonstrate that DREAM-GAN significantly improves the vanilla DREAMPlace at the placement stage across each benchmark, but also show that the improvements last firmly to the post-route stage, where we observe improvements by up to 8.3% in wirelength and 7.4% in total power.

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Cited By

Lu YRen HHsiao HLim S(2024)GAN-Place: Advancing Open Source Placers to Commercial-quality Using Generative Adversarial Networks and Transfer LearningACM Transactions on Design Automation of Electronic Systems10.1145/363646129:2(1-17)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.1145/3636461
Liang RAgnesina APradipta GChhabria VRen H(2023)Invited Paper: CircuitOps: An ML Infrastructure Enabling Generative AI for VLSI Circuit Optimization2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323611(1-6)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323611

Index Terms

DREAM-GAN: Advancing DREAMPlace towards Commercial-Quality using Generative Adversarial Learning
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)
      1. Placement

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Published In

cover image ACM Conferences

ISPD '23: Proceedings of the 2023 International Symposium on Physical Design

March 2023

278 pages

ISBN:9781450399784

DOI:10.1145/3569052

General Chair:
David Chinnery
Siemens Digital Industries Software, USA
,
Program Chair:
Iris Hui-Ru Jiang
National Taiwan University, Taiwan

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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SIGDA: ACM Special Interest Group on Design Automation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 March 2023

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ISPD '23

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ISPD '23: International Symposium on Physical Design

March 26 - 29, 2023

Virtual Event, USA

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Overall Acceptance Rate 62 of 172 submissions, 36%

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ISPD '25

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Cited By

Lu YRen HHsiao HLim S(2024)GAN-Place: Advancing Open Source Placers to Commercial-quality Using Generative Adversarial Networks and Transfer LearningACM Transactions on Design Automation of Electronic Systems10.1145/363646129:2(1-17)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.1145/3636461
Liang RAgnesina APradipta GChhabria VRen H(2023)Invited Paper: CircuitOps: An ML Infrastructure Enabling Generative AI for VLSI Circuit Optimization2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323611(1-6)Online publication date: 28-Oct-2023
https://doi.org/10.1109/ICCAD57390.2023.10323611

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