invited-talk

Why are Graph Neural Networks Effective for EDA Problems?: (Invited Paper)

Authors:

Siddhartha Nath,

Mingjie LiuAuthors Info & Claims

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

Article No.: 1, Pages 1 - 8

https://doi.org/10.1145/3508352.3561093

Published: 22 December 2022 Publication History

Abstract

In this paper, we discuss the source of effectiveness of Graph Neural Networks (GNNs) in EDA, particularly in the VLSI design automation domain. We argue that the effectiveness comes from the fact that GNNs implicitly embed the prior knowledge and inductive biases associated with given VLSI tasks, which is one of the three approaches to make a learning algorithm physics-informed. These inductive biases are different to those common used in GNNs designed for other structured data, such as social networks and citation networks. We will illustrate this principle with several recent GNN examples in the VLSI domain, including predictive tasks such as switching activity prediction, timing prediction, parasitics prediction, layout symmetry prediction, as well as optimization tasks such as gate sizing and macro and cell transistor placement. We will also discuss the challenges of applications of GNN and the opportunity of applying self-supervised learning techniques with GNN for VLSI optimization.

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Jiang WZhao ZLuo ZZhou JZhang SHu BBian P(2024)TSTL-GNN: Graph-Based Two-Stage Transfer Learning for Timing Engineering Change Order Analysis AccelerationElectronics10.3390/electronics1315289713:15(2897)Online publication date: 23-Jul-2024
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https://dl.acm.org/doi/10.1016/j.vlsi.2024.102211
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Why are Graph Neural Networks Effective for EDA Problems?: (Invited Paper)
1. Hardware

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Information & Contributors

Information

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: 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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https://doi.org/10.1016/j.vlsi.2024.102316
Jiang WZhao ZLuo ZZhou JZhang SHu BBian P(2024)TSTL-GNN: Graph-Based Two-Stage Transfer Learning for Timing Engineering Change Order Analysis AccelerationElectronics10.3390/electronics1315289713:15(2897)Online publication date: 23-Jul-2024
https://doi.org/10.3390/electronics13152897
Wu HHuang ZLi XZhu W(2024)AiTOIntegration, the VLSI Journal10.1016/j.vlsi.2024.10221198:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.vlsi.2024.102211
Lopera DEcker W(2024)AI-Enabled Static Timing Analysis at Early Stages of the Digital Design FlowAI-Enabled Electronic Circuit and System Design10.1007/978-3-031-71436-8_4(109-154)Online publication date: 17-Oct-2024
https://doi.org/10.1007/978-3-031-71436-8_4
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
Sánchez Lopera DSubedi IEcker W(2023)Using Graph Neural Networks for Timing Estimations of RTL Intermediate Representations2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD)10.1109/MLCAD58807.2023.10299859(1-6)Online publication date: 10-Sep-2023
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Min KKwon SLee SKim DPark SKang S(2023)ClusterNet: Routing Congestion Prediction and Optimization Using Netlist Clustering and Graph Neural Networks2023 IEEE/ACM International Conference on Computer Aided Design (ICCAD)10.1109/ICCAD57390.2023.10323942(1-9)Online publication date: 28-Oct-2023
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Zhu LGu YGuo X(2023)RC-GNN: Fast and Accurate Signoff Wire Delay Estimation with Customized Graph Neural Networks2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)10.1109/AICAS57966.2023.10168562(1-5)Online publication date: 11-Jun-2023
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Li YXue CZargari FLi Y(2023)From Graph Theory to Graph Neural Networks (GNNs): The Opportunities of GNNs in Power ElectronicsIEEE Access10.1109/ACCESS.2023.334579511(145067-145084)Online publication date: 2023
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