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

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.