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Report for NSF Workshop on AI for Electronic Design Automation [NSF Workshop Report]
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DOI：10.1109/MCAS.2026.3662307.png)
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Abstract
En 中文
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 website <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://ai4eda-workshop.github.io/</uri>
Keywords:
AI for electronic design automation
chip design
workshop report
physical design
high-level synthesis
logic-level synthesis
test and verification
large language model
graph neural network
reinforcement learning (RL)
neurosymbolic AI
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