Machine Learning for Design Methodology and EDA Optimization.
Abstract: In this talk, I will first illustrate how ML helps improve design quality as well as design productivity from design methodology perspective with examples in digital and analog designs. Then I will discuss the potential of applying ML to solve challenging EDA optimization problems, focusing on three promising ML techniques: reinforcement learning (RL), physics-based modeling and self-supervised learning (SSL). RL learns to optimize the problem by converting the EDA problem objectives into environment rewards. It can be applied to both directly solve the EDA problem or be part of a conventional EDA algorithm. Physics-based modeling enables more accurate and transferable learning for EDA problems. SSL learns the optimized EDA solution data manifold. Conditioned on the problem input, it can directly produce the solution. I will illustrate the applications of these techniques in standard cell layout, computational lithography, and gate sizing problems. Finally, I will outline three main approaches to integrate ML and conventional EDA algorithms together and the importance of adopting GPU computing to EDA.