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invited-talk

Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs

Authors:

Johann Knechtel,

Ozgur SinanogluAuthors Info & Claims

ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference

Pages 83 - 90

https://doi.org/10.1145/3566097.3568345

Published: 31 January 2023 Publication History

Abstract

Graph neural networks (GNNs) have pushed the state-of-the-art (SOTA) for performance in learning and predicting on large-scale data present in social networks, biology, etc. Since integrated circuits (ICs) can naturally be represented as graphs, there has been a tremendous surge in employing GNNs for machine learning (ML)-based methods for various aspects of IC design. Given this trajectory, there is a timely need to review and discuss some powerful and versatile GNN approaches for advancing IC design.

In this paper, we propose a generic pipeline for tailoring GNN models toward solving challenging problems for IC design. We outline promising options for each pipeline element, and we discuss selected and promising works, like leveraging GNNs to break SOTA logic obfuscation. Our comprehensive overview of GNNs frameworks covers (i) electronic design automation (EDA) and IC design in general, (ii) design of reliable ICs, and (iii) design as well as analysis of secure ICs. We provide our overview and related resources also in the GNN4IC hub at https://github.com/DfX-NYUAD/GNN4IC. Finally, we discuss interesting open problems for future research.

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https://dl.acm.org/doi/10.1145/3658617.3697701
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Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs
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cover image ACM Conferences

ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference

January 2023

807 pages

ISBN:9781450397834

DOI:10.1145/3566097

General Chair:
Atsushi Takahashi
Tokyo Institute of Technology

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Published: 31 January 2023

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ASPDAC '23: 28th Asia and South Pacific Design Automation Conference

January 16 - 19, 2023

Tokyo, Japan

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ASPDAC '23 Paper Acceptance Rate 102 of 328 submissions, 31%;

Overall Acceptance Rate 466 of 1,454 submissions, 32%

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Cited By

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https://dl.acm.org/doi/10.1145/3658617.3697701
Bajpai AYadav SNagwani N(2025)An extensive bibliometric analysis of artificial intelligence techniques from 2013 to 2023The Journal of Supercomputing10.1007/s11227-025-07021-381:4Online publication date: 24-Feb-2025
https://doi.org/10.1007/s11227-025-07021-3
Genssler PAlrahis LSinanoglu OAmrouch H(2024)HDCircuit: Brain-Inspired HyperDimensional Computing for Circuit Recognition2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546587(1-2)Online publication date: 25-Mar-2024
https://doi.org/10.23919/DATE58400.2024.10546587
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
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
Berndt AMeinhardt C(2024)CeConP: Exploring Node Centrality for Early Routing Congestion Prediction2024 IEEE 15th Latin America Symposium on Circuits and Systems (LASCAS)10.1109/LASCAS60203.2024.10506148(1-5)Online publication date: 27-Feb-2024
https://doi.org/10.1109/LASCAS60203.2024.10506148
Li ZYang LLou M(2024)DAGNN-RE: Directed Acyclic Graph Neural Network for Functional Reverse Engineering of Gate-Level NetlistIntegration10.1016/j.vlsi.2024.102343(102343)Online publication date: Dec-2024
https://doi.org/10.1016/j.vlsi.2024.102343
Gandhi JShekhawat DSantosh MPandey J(2024)GAN4IP: A unified GAN and logic locking-based pipeline for hardware IP securitySādhanā10.1007/s12046-024-02461-849:2Online publication date: 5-May-2024
https://doi.org/10.1007/s12046-024-02461-8
Hosseintalaee HJahanian AAlizadeh B(2023)Systematic Trojan Detection in Crypto-Systems Using the Model CheckerJournal of Circuits, Systems and Computers10.1142/S021812662450045233:03Online publication date: 5-Oct-2023
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Alrahis LSinanoglu O(2023)Graph Neural Networks for Hardware Vulnerability Analysis— Can you Trust your GNN?2023 IEEE 41st VLSI Test Symposium (VTS)10.1109/VTS56346.2023.10140095(1-4)Online publication date: 24-Apr-2023
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