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

arXiv: https://arxiv.org/abs/2211.16495 · HTML: https://mlsanity.com/paper/2211.16495/

## 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 (most cited first, 30 of 40)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [Link Prediction Based on Graph Neural Networks](https://mlsanity.com/paper/1802.09691/) (2018)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [Principal Neighbourhood Aggregation for Graph Nets](https://mlsanity.com/paper/2004.05718/) (2020)
- [GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning](https://mlsanity.com/paper/2005.00406/) (2020)
- [Machine Learning for Electronic Design Automation: A Survey](https://mlsanity.com/paper/2102.03357/) (2021)
- [On Joint Learning for Solving Placement and Routing in Chip Design](https://mlsanity.com/paper/2111.00234/) (2021)
- [IronMan: GNN-assisted Design Space Exploration in High-Level Synthesis via Reinforcement Learning](https://mlsanity.com/paper/2102.08138/) (2021)
- [High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing](https://mlsanity.com/paper/2201.06848/) (2022)
- [Generalizable Cross-Graph Embedding for GNN-based Congestion Prediction](https://mlsanity.com/paper/2111.05941/) (2021)
- [HW2VEC: A Graph Learning Tool for Automating Hardware Security](https://mlsanity.com/paper/2107.12328/) (2021)
- [GNN4IP: Graph Neural Network for Hardware Intellectual Property Piracy Detection](https://mlsanity.com/paper/2107.09130/) (2021)
- [Net2: A Graph Attention Network Method Customized for Pre-Placement Net Length Estimation](https://mlsanity.com/paper/2011.13522/) (2020)
- [Hardware Trojan Detection using Graph Neural Networks](https://mlsanity.com/paper/2204.11431/) (2022)
- [LOSTIN: Logic Optimization via Spatio-Temporal Information with Hybrid Graph Models](https://mlsanity.com/paper/2201.08455/) (2022)
- [AppGNN: Approximation-Aware Functional Reverse Engineering using Graph Neural Networks](https://mlsanity.com/paper/2208.10868/) (2022)
- [Challenging the Security of Logic Locking Schemes in the Era of Deep Learning: A Neuroevolutionary Approach](https://mlsanity.com/paper/2011.10389/) (2020)
- [GNNUnlock: Graph Neural Networks-based Oracle-less Unlocking Scheme for Provably Secure Logic Locking](https://mlsanity.com/paper/2012.05948/) (2020)
- [MuxLink: Circumventing Learning-Resilient MUX-Locking Using Graph Neural Network-based Link Prediction](https://mlsanity.com/paper/2112.07178/) (2021)
- [Protect Your Chip Design Intellectual Property: An Overview](https://mlsanity.com/paper/1902.05333/) (2019)
- [Estimating the Circuit Deobfuscating Runtime based on Graph Deep Learning](https://mlsanity.com/paper/1902.05357/) (2019)
- [Hardware Security for and beyond CMOS Technology](https://mlsanity.com/paper/2001.08780/) (2020)
- [Towards Secure Composition of Integrated Circuits and Electronic Systems: On the Role of EDA](https://mlsanity.com/paper/2001.09672/) (2020)
- [UNSAIL: Thwarting Oracle-Less Machine Learning Attacks on Logic Locking](https://mlsanity.com/paper/2012.14938/) (2020)
- [GNN4REL: Graph Neural Networks for Predicting Circuit Reliability Degradation](https://mlsanity.com/paper/2208.02868/) (2022)
- [Deceptive Logic Locking for Hardware Integrity Protection against Machine Learning Attacks](https://mlsanity.com/paper/2107.08695/) (2021)

## Built on by (most cited first, 3 of 3)

- [BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation](https://mlsanity.com/paper/2401.10753/) (2024)
- [Estimating Voltage Drop: Models, Features and Data Representation Towards a Neural Surrogate](https://mlsanity.com/paper/2502.05345/) (2025)
- [PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning](https://mlsanity.com/paper/2503.03747/) (2025)

## 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.