论文标题
黑暗的一面：EDA机器学习的安全问题
The Dark Side: Security Concerns in Machine Learning for EDA
论文作者
论文摘要
日益增长的IC复杂性导致了通过新的电子设计自动化（EDA）方法的迫切需要提高设计效率的需求。近年来，通过机器学习（ML）技术启用了许多前所未有的高效EDA方法。尽管ML在电路设计中表现出巨大的潜力，但是很少讨论有关安全问题的黑暗面。本文对我们在EDA的ML中观察到的所有安全问题提供了全面和公正的摘要。其中许多人被该领域的从业者隐藏或忽视了。在本文中，我们首先提供分类法来定义四种主要的安全问题类型，然后分析EDA中ML中不同的应用程序方案和特殊属性。之后，我们通过实验介绍了对每个安全问题的详细分析。
The growing IC 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 security problems, is seldomly discussed. This paper gives a comprehensive and impartial summary of all security concerns we have observed in ML for EDA. Many of them are hidden or neglected by practitioners in this field. In this paper, we first provide our taxonomy to define four major types of security concerns, then we analyze different application scenarios and special properties in ML for EDA. After that, we present our detailed analysis of each security concern with experiments.
