Machine Learning Applications in Physical Design: Recent Results and Directions

In the late-CMOS era, semiconductor and electronics companies face severe product schedule and other competitive pressures. In this context, electronic design automation (EDA) must deliver "design-based equivalent scaling" to help continue essential industry trajectories. A powerful lever for this will be the use of machine learning techniques, both inside and "around" design tools and flows. This paper reviews opportunities for machine learning with a focus on IC physical implementation. Example applications include (1) removing unnecessary design and modeling margins through correlation mechanisms, (2) achieving faster design convergence through predictors of downstream flow outcomes that comprehend both tools and design instances, and (3) corollaries such as optimizing the usage of design resources licenses and available schedule. The paper concludes with open challenges for machine learning in IC physical design.