Report for NSF Workshop on AI for Electronic Design Automation [NSF Workshop Report]

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Authors: Deming Chen, Vijay Ganesh, Weikai Li, Yingyan Lin, Yong Liu, Subhasish Mitra, David Z. Pan, Ruchir Puri, Jason Cong, Yizhou Sun
Cited by: 2
References: 41

Abstract

This report distills the discussions and recommendations from the NSF Workshop on AI for Electronic Design Automation (EDA), held on December 10, 2024 in Vancouver along-side NeurIPS 2024. Bringing together experts across machine learning and EDA, the workshop examined how AI—spanning large language models (LLMs), graph neural networks (GNNs), reinforcement learning (RL), neurosymbolic methods, etc.—can facilitate EDA and shorten design turnaround. The workshop includes four themes: (1) AI for physical synthesis and design for manufacturing (DFM), discussing challenges in physical manufacturing process and potential AI applications; (2) AI for high-level and logic-level synthesis (HLS/LLS), covering pragma insertion, program transformation, RTL code generation, etc.; (3) AI toolbox for optimization and design, discussing frontier AI developments that could potentially be applied to EDA tasks; and (4) AI for test and verification, including LLM-assisted verification tools, ML-augmented SAT solving, security/reliability challenges, etc. The report recommends NSF to foster AI/EDA collaboration, invest in foundational AI for EDA, develop robust data infrastructures, promote scalable compute infrastructure, and invest in workforce development to democratize hardware design and enable next-generation hardware systems. The workshop information can be found on the websitehttps://ai4eda-workshop.github.io/

Fields of Study & Subjects

Machine Learning in Materials Science Embedded Systems Design Techniques VLSI and FPGA Design Techniques

Publication details

Journal / source
IEEE Circuits and Systems Magazine
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Volume
26
Issue
1
Pages
67–81
Publication date
1 January 2026