Cunxi Yu

University of Maryland - College Park / NVIDIA Research, USA

The Rise and Fall of Machine Learning for Computing and Optimization

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 applied ML methods to verification and synthesis to algorithmic learning and general combinatorial optimizations.

Biography

Cunxi is an Assistant Professor at the University of Maryland, College Park, and holds Visiting Professorship at NVIDIA Research. 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 (with Prof. Zhiru Zhang) and EPFL (with Prof. Giovanni De Micheli). His work has received the Best Paper Award at ASPLOS 20205, Best Paper Award at DAC (2023), NSF CAREER Award (2021), American Physical Society DLS poster award (2022), and best paper nominations at ASP-DAC and DAC. Cunxi earned his Ph.D. from UMass Amherst in 2017 with Prof. Maciej Ciesielski.

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