Andrew B. Kahng
Solvers, Engines, Tools and Flows: The Next Wave for AI/ML in Physical Design
ISPD ’24 Proceedings
March 12–15, 2024, Taipei, Taiwan.
DOI 10.1145/3626184.3635277
Abstract: It has been six years since an ISPD-2018 invited talk on “Machine Learning Applications in Physical Design”. Since then, despite considerable activity across both academia and industry, many R&D targets remain open. At the same time, there is now clearer understanding of where AI/ML can and cannot (yet) move the needle in physical design, as well as some of the difficult blockers and technical challenges that lie ahead. Some futures for AI/ML-boosted physical design are visible across solvers, engines, tools and flows and in contexts that span generative AI, the modeling of “magic” handoffs at flow interstices, academic research infrastructure, and the culture of benchmarking and open-source EDA.
Fig: OpenROAD as a new EDA playground for ML researchers
Acknowledgments: Many thanks to Sayak Kundu, Bodhisatta Pramanik, Zhiang Wang and Dooseok Yoon for their help with the figures and text in this paper. Discussions with Siddhartha Nath, Igor Markov, Chuck Alpert and Ilgweon Kang are also gratefully acknowledged. Research at UCSD is partially supported by DARPA, Samsung, the C-DEN center, and gifts from Google, Intel and others.
