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 alongside 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 https://ai4eda-workshop.github.io/.

翻译：本报告总结了2024年12月10日在温哥华与NeurIPS 2024同期举行的NSF人工智能电子设计自动化(EDA)研讨会的讨论与建议。汇聚机器学习与EDA领域的专家，研讨会探讨了人工智能——涵盖大语言模型(LLMs)、图神经网络(GNNs)、强化学习(RL)、神经符号方法等——如何促进EDA并缩短设计周期。研讨会包含四个主题：(1) 人工智能在物理综合与可制造性设计(DFM)中的应用，讨论了物理制造过程中的挑战及潜在人工智能应用；(2) 人工智能在高级综合与逻辑级综合(HLS/LLS)中的应用，涵盖编译指示插入、程序转换、RTL代码生成等；(3) 面向优化与设计的人工智能工具箱，探讨可能应用于EDA任务的前沿人工智能发展；(4) 人工智能在测试与验证中的应用，包括LLM辅助验证工具、机器学习增强的SAT求解、安全/可靠性挑战等。报告建议NSF促进人工智能/EDA合作、投资基础人工智能在EDA领域的研究、开发稳健的数据基础设施、推动可扩展计算基础设施，并投资人才培养以普及硬件设计并实现下一代硬件系统。研讨会信息可在网站 https://ai4eda-workshop.github.io/ 上获取。