Machine Learning in EDA: Opportunities and Challenges
Elias Fallon · 2020
Electronic Design Automation software has delivered semiconductor design productivity improvements for decades. The next leap in productivity will come from the addition of machine learning techniques to the toolbox of computational software capabilities employed by EDA developers. Recent research and development into machine learning for EDA point to clear patterns for how it impacts EDA tools, flows, and design challenges. This research has also illustrated some of the challenges that will come with production deployment of machine learning techniques into EDA tools and flows. This talk will detail patterns observed in ML for EDA development, as well as discussing challenges with productization of ML for EDA developments and the opportunities that it presents for researchers.