The Dark Side: Security and Reliability Concerns in Machine Learning for EDA
The Dark Side: Security and Reliability Concerns in Machine Learning for EDA
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DOI：
10.1109/tcad.2022.3199172
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发表时间：
2023-04
影响因子：
2.9
通讯作者：
Zhiyao Xie;Jingyu Pan;Chen-Chia Chang;Jiangkun Hu;Yiran Chen
中科院分区：
文献类型：
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作者：
Zhiyao Xie;Jingyu Pan;Chen-Chia Chang;Jiangkun Hu;Yiran Chen
集成电路复杂性的不断增长导致了对通过新的电子设计自动化（EDA）方法来提高设计效率的迫切需求。近年来，许多前所未有的高效EDA方法已经通过机器学习（ML）技术实现。尽管ML在电路设计中显示出巨大的潜力，但其潜在的安全性和模型可靠性问题却很少被讨论。本文对我们在ML for EDA中观察到的所有安全性和可靠性问题进行了全面而公正的总结。其中许多被这一领域的从业人员隐藏或忽视。在本文中，我们首先提供我们的分类法来定义四种主要类型的关注点，然后我们分析了ML for EDA中的不同应用场景和特殊属性。之后，我们提出了我们的详细和公正的分析，每一种类型的关注与实验。
The growing integrated circuit complexity has led to a compelling need for design efficiency improvement through new electronic design automation (EDA) methodologies. In recent years, many unprecedented efficient EDA methods have been enabled by machine learning (ML) techniques. While ML demonstrates its great potential in circuit design, however, the dark side about potential security and model reliability problems, is seldomly discussed. This article gives a comprehensive and impartial summary of all security and reliability concerns we have observed in ML for EDA. Many of them are hidden or neglected by practitioners in this field. In this article, we first provide our taxonomy to define four major types of concerns, then we analyze different application scenarios and special properties in ML for EDA. After that, we present our detailed and impartial analysis of each type of concern with experiments.