December 6
@
12:00 PM
–
1:00 PM
4610 Engineering Hall
The Rise and Fall of Machine Learning for EDA – Studies in Synthesis and Verification
Abstract: In recent years, Machine Learning (ML) has gained considerable momentum in electronic design automation (EDA). Specifically, the successes of ML-driven EDA methods and infrastructure have demonstrated its unique capability in capturing the multitude of factors affecting estimation accuracy, effectively exploring large algorithmic and design spaces in synthesis, and accelerating classical combinatorial optimization problems. In particular, synthesis and verification, critical stages in EDA, have significantly benefited from ML in the last five years. However, during the development of ML-driven synthesis and verification approaches, several points of convergence have been observed, including practicality, system engineering challenges, data availability, and determinism. In this talk, I will present the journey of exploring ML in synthesis and verification, focusing on discussing the evolutionary developments from static ML-based synthesis approaches to algorithmic learning and general combinatorial optimizations using advanced domain-specific ML techniques. The talk will primarily focus on our recent work in high-level synthesis, logic synthesis, and Boolean reasoning.
Bio: Cunxi is an Assistant Professor at the University of Maryland, College Park. His research interests center around novel algorithms, systems, and hardware designs for computing and security. Before joining the University of Maryland, Cunxi was an Assistant Professor at the University of Utah and held a PostDoc position at Cornell University. His work has received the Best Paper Award at DAC (2023), the NSF CAREER Award (2021), American Physical Society DLS poster award (2022), and multiple best paper nominations. Cunxi earned his Ph.D. from UMass Amherst in 2017.