invited-talk

On Advancing Physical Design Using Graph Neural Networks

Authors:

Sung Kyu LimAuthors Info & Claims

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

Article No.: 2, Pages 1 - 7

https://doi.org/10.1145/3508352.3561094

Published: 22 December 2022 Publication History

Abstract

As modern Physical Design (PD) algorithms and methodologies evolve into the post-Moore era with the aid of machine learning, Graph Neural Networks (GNNs) are becoming increasingly ubiquitous given that netlists are essentially graphs. Recently, their ability to perform effective graph learning has provided significant insights to understand the underlying dynamics during netlist-to-layout transformations. GNNs follow a message-passing scheme, where the goal is to construct meaningful representations either at the entire graph or node-level by recursively aggregating and transforming the initial features. In the realm of PD, the GNN-learned representations have been leveraged to solve the tasks such as cell clustering, quality-of-result prediction, activity simulation, etc., which often overcome the limitations of traditional PD algorithms. In this work, we first revisit recent advancements that GNNs have made in PD. Second, we discuss how GNNs serve as the backbone of novel PD flows. Finally, we present our thoughts on ongoing and future PD challenges that GNNs can tackle and succeed.

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Cited By

Zhang RRajarathnam RPan DKoushanfar FAmrouch HHu JGarg SLin Y(2024)Automated Physical Design Watermarking Leveraging Graph Neural NetworksProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685951(1-10)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685951
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
Zhang RRajarathnam RPan DKoushanfar F(2024)Automated Physical Design Watermarking Leveraging Graph Neural Networks2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740234(1-10)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740234
Show More Cited By

Index Terms

On Advancing Physical Design Using Graph Neural Networks
1. Applied computing
  1. Arts and humanities
    1. Architecture (buildings)
      1. Computer-aided design
  2. Physical sciences and engineering
    1. Engineering
2. Hardware
  1. Electronic design automation
    1. Physical design (EDA)
  2. Emerging technologies

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

ICCAD '22: Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design

October 2022

1467 pages

ISBN:9781450392174

DOI:10.1145/3508352

Conference Chair:
Tulika Mitra
National University of Singapore
,
Program Chairs:
Evangeline Young
The Chinese University of Hong Kong
,
Jinjun Xiong
University at Buffalo (UB)

Copyright © 2022 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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SIGDA: ACM Special Interest Group on Design Automation

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IEEE-EDS: Electronic Devices Society
IEEE CAS
IEEE CEDA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 December 2022

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ICCAD '22

Sponsor:

SIGDA

ICCAD '22: IEEE/ACM International Conference on Computer-Aided Design

October 30 - November 3, 2022

California, San Diego

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Overall Acceptance Rate 457 of 1,762 submissions, 26%

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Cited By

Zhang RRajarathnam RPan DKoushanfar FAmrouch HHu JGarg SLin Y(2024)Automated Physical Design Watermarking Leveraging Graph Neural NetworksProceedings of the 2024 ACM/IEEE International Symposium on Machine Learning for CAD10.1145/3670474.3685951(1-10)Online publication date: 9-Sep-2024
https://dl.acm.org/doi/10.1145/3670474.3685951
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
Zhang RRajarathnam RPan DKoushanfar F(2024)Automated Physical Design Watermarking Leveraging Graph Neural Networks2024 ACM/IEEE 6th Symposium on Machine Learning for CAD (MLCAD)10.1109/MLCAD62225.2024.10740234(1-10)Online publication date: 9-Sep-2024
https://doi.org/10.1109/MLCAD62225.2024.10740234
Hu YLi JKlemme FNam GMa TAmrouch HXiong JOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)SyncTREEProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667057(21415-21428)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667057
Lu YChan WGuo DKundu SKhandelwal VLim S(2023)RL-CCD: Concurrent Clock and Data Optimization using Attention-Based Self-Supervised Reinforcement Learning2023 60th ACM/IEEE Design Automation Conference (DAC)10.1109/DAC56929.2023.10248008(1-6)Online publication date: 9-Jul-2023
https://doi.org/10.1109/DAC56929.2023.10248008

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