Machine-Learning Enabled Next-Generation Physical Design - An EDA Perspective

Abstract

Physical design is an ensemble of NP-complete problems that P&R tools attempt to solve in (pseudo) linear time. Advanced process nodes and complex signoff requirements bring in new physical and timing constraints into the implementation flow, making it harder for physical design algorithms to deliver industry-leading power, performance, area (PPA), without giving up design turn-around-time. The relentless pursuit for low-power high-performance designs is putting constant pressure to limit any over-design, creating an acute need to have better models/predictions and advanced analytics to drive implementation flows. Given the advancements in supervised and reinforcement learning, combined with the availability of large-scale compute, Machine Learning (ML) has the potential to become a disruptive paradigm change for EDA tools. In this talk, I would like to share some of the challenges and opportunities for innovation in next-generation physical design using ML.Biography: Vishal leads the physical optimization team for the Digital Implementation products at Synopsys. He has 15 years of R&D experience in building state-of-the-art optimization engines and P&R flows targeting advanced-node low-power high-performance designs. More recently, he has been looking at bringing machine-learning paradigms into digital implementation tools to improve power, performance, area and productivity. Vishal has a B.Tech. from Indian Institute of Technology, Kanpur and a Ph.D. from University of Maryland, College Park. He has won a best paper award at ISPD, co-authored several patents and over 20 IEEE/ACM publications.