mlsanityData to 14 Oct 2026

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

arXiv 2211.16495 ↗First posted 29 Nov 2022cs.LG cs.AR cs.CRcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 40 of its 101 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

40 of 101 references.

References · 40

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2017Graph Attention Networkscited by 8,923
  3. 2017Inductive Representation Learning on Large Graphscited by 6,320
  4. 2018How Powerful are Graph Neural Networks?cited by 4,393
  5. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  6. 2018Link Prediction Based on Graph Neural Networkscited by 832
  7. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  8. 2020Principal Neighbourhood Aggregation for Graph Netscited by 478
  9. 2020GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learningcited by 89
  10. 2021Machine Learning for Electronic Design Automation: A Surveycited by 72
  11. 2021On Joint Learning for Solving Placement and Routing in Chip Designcited by 44
  12. 2021IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learningcited by 26
28 more
  1. 2022High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancingcited by 26
  2. 2021Generalizable Cross-Graph Embedding for GNN-based Congestion Predictioncited by 17
  3. 2021HW2VEC: A Graph Learning Tool for Automating Hardware Securitycited by 16
  4. 2021GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detectioncited by 14
  5. 2020Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimationcited by 8
  6. 2022Hardware Trojan Detection using Graph Neural Networkscited by 8
  7. 2022LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Modelscited by 7
  8. 2022AppGNN: Approximation-Aware Functional Reverse Engineering using Graph Neural Networkscited by 7
  9. 2020Challenging the Security of Logic Locking Schemes in the Era of Deep Learning: A Neuroevolutionary Approachcited by 6
  10. 2020GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Lockingcited by 5
  11. 2021MuxLink: Circumventing Learning-Resilient MUX-Locking Using Graph Neural Network-based Link Predictioncited by 5
  12. 2019Protect Your Chip Design Intellectual Property: An Overviewcited by 4
  13. 2019Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learningcited by 4
  14. 2020Hardware Security for and beyond CMOS Technologycited by 4
  15. 2020Towards Secure Composition of Integrated Circuits and Electronic Systems: On the Role of EDAcited by 4
  16. 2020UNSAIL: Thwarting Oracle-Less Machine Learning Attacks on Logic Lockingcited by 4
  17. 2022GNN4REL: Graph Neural Networks for Predicting Circuit Reliability Degradationcited by 4
  18. 2021Deceptive Logic Locking for Hardware Integrity Protection against Machine Learning Attackscited by 3
  19. 2021ReIGNN: State Register Identification Using Graph Neural Networks for Circuit Reverse Engineeringcited by 3
  20. 2020InterLock: An Intercorrelated Logic and Routing Lockingcited by 2
  21. 2021UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Predictioncited by 2
  22. 2022Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Networkcited by 2
  23. 2022Embracing Graph Neural Networks for Hardware Security (Invited Paper)cited by 2
  24. 2017Obfuscating the Interconnects: Low-Cost and Resilient Full-Chip Layout Camouflagingcited by 1
  25. 2018Advancing Hardware Security Using Polymorphic and Stochastic Spin-Hall Effect Devicescited by 1
  26. 2019ScanSAT: Unlocking Static and Dynamic Scan Obfuscationcited by 1
  27. 2021Logic Locking at the Frontiers of Machine Learning: A Survey on Developments and Opportunitiescited by 1
  28. 2022Contrastive Graph Convolutional Networks for Hardware Trojan Detection in Third Party IP Corescited by 1

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulationcited by 2
  2. 2025Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogatecited by 0
  3. 2025PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoningcited by 0