The Dark Side: Security and Reliability Concerns in Machine Learning for EDA

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.