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.
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CITATION STYLE
Kahng, A. B. (2024). Solvers, Engines, Tools and Flows: The Next Wave for AI/ML in Physical Design. In Proceedings of the International Symposium on Physical Design (pp. 117–124). Association for Computing Machinery. https://doi.org/10.1145/3626184.3635277
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