Understanding Graphs in EDA: From Shallow to Deep Learning

Abstract

As the scale of integrated circuits keeps increasing, it is witnessed that there is a surge in the research of electronic design automation (EDA) to make the technology node scaling happen. Graph is of great significance in the technology evolution since it is one of the most natural ways of abstraction to many fundamental objects in EDA problems like netlist and layout, and hence many EDA problems are essentially graph problems. Traditional approaches for solving these problems are mostly based on analytical solutions or heuristic algorithms, which require substantial efforts in designing and tuning. With the emergence of the learning techniques, dealing with graph problems with machine learning or deep learning has become a potential way to further improve the quality of solutions. In this paper, we discuss a set of key techniques for conducting machine learning on graphs. Particularly, a few challenges in applying graph learning to EDA applications are highlighted. Furthermore, two case studies are presented to demonstrate the potential of graph learning on EDA applications.