“The Rise and Fall of Machine Learning for EDA and Combinatorial Optimization”

In recent years, Machine Learning (ML) has gained considerable momentum in electronic design automation (EDA). ML-driven methods and infrastructures have demonstrated a unique capability to capture the multitude of factors affecting estimation accuracy, effectively explore large algorithmic and design spaces in synthesis, and accelerate classical combinatorial optimization problems. In particular, synthesis and verification, two critical stages in EDA, have significantly benefited from ML over the past five years. However, the development of ML-driven synthesis and verification approaches has also revealed several points of convergence, including challenges in practicality, system engineering, data availability, and determinism. In this talk, I will present the journey of ML in synthesis and verification, highlighting its evolution from static ML-based approaches to algorithmic learning and general combinatorial optimizations enabled by differentiable programming, ML infrastructures, and specialized hardware. I will also discuss the emerging role of large language models (LLMs) in combinatorial optimization.

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 ASPLOS ’25, the Best Paper Award at DAC ’23, and Best Paper Finalist at DAC ’25, as well as the NSF CAREER Award (2021), the American Physical Society DLS Poster Award, and multiple Best Paper Nominations. Cunxi earned his Ph.D. from UMass Amherst.