论文标题
图形神经网络：一种功能强大且通用的工具，用于提高IC的设计，可靠性和安全性
Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs
论文作者
论文摘要
图形神经网络（GNNS）已推动了最先进的（SOTA）在学习和预测社交网络，生物学等中存在的大规模数据方面的性能。由于综合电路（ICS）自然可以作为图表表示，因此在机器学习（ML）的各个方面都有巨大的兴趣，在使用GNN方面有巨大的兴趣。鉴于这一轨迹，有及时需要审查并讨论一些强大而多才多艺的GNN方法，以推进IC设计。 在本文中，我们提出了一条通用管道，用于调整GNN模型来解决IC设计的具有挑战性的问题。我们概述了每个管道元素的有希望的选项，并讨论了选定和有希望的作品，例如利用GNNS打破SOTA逻辑混淆。我们对GNNS框架的全面概述涵盖了（i）电子设计自动化（EDA）和IC设计，（ii）可靠IC的设计以及（iii）设计以及对安全IC的分析。我们在https://github.com/dfx-nyuad/gnn4ic的GNN4IC中心也提供了概述和相关资源。最后，我们讨论了未来研究的有趣的开放问题。
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.
