Security and Reliability Challenges in Machine Learning for EDA: Latest Advances

Zhiyao Xie, Tao Zhang, Yifeng Peng
Hong Kong University of Science and Technology


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 innovative machine learning (ML)-based solutions have been proposed for EDA applications. While these ML solutions demonstrate great potential in the circuit design flow, however, the hidden security and model reliability problems are rarely discussed until recently. In this paper, we present some latest research advances in the security and reliability challenges in ML for EDA.