Machine Learning in EDA: When and How

Machine learning is a powerful technique that can derive knowledge from large data set, and provide prediction and modeling. Since VLSI chip designs have extremely high complexity and gigantic data, recently there has been a surge in applying and adapting machine learning to accelerate the design closure. In this talk, I will discuss when and how to apply machine learning in Electronic Design Automation (EDA) improving the efficiency and quality of the design process. Furthermore, I highlight distinct challenges in EDA, including improved netlist representation, advanced timing modeling, netlist-layout multimodality, and constrained AIGC.