Machine learning and systems for building the next generation of EDA tools

This paper describes how machine learning techniques enable the development of the next generation of EDA tools with substantial gains in performance and ease-of-use. After a brief overview of machine learning techniques, we discuss performance limitations of traditional compute and storage systems, and the systems and infrastructure considerations for performing machine learning at scale. We conclude with a few examples in which machine learning can be applied to solve common optimization and classification problems encountered in the traditional CAD flows, including functional verification and debug.