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 paper, we will discuss when and how to apply machine learning in Electronic Design Automation (EDA) improving the efficiency and quality of the design process. Furthermore, we highlight distinct challenges in EDA, including improved netlist representation, advanced timing modeling, netlist-layout multimodality, and constrained AIGC.