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：本报告提炼了2024年12月10日于温哥华NeurIPS 2024会议同期举行的美国国家科学基金会（NSF）“人工智能赋能电子设计自动化（EDA）”研讨会的讨论与建议。研讨会汇聚了机器学习与EDA领域的专家，深入探讨了人工智能技术——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流程并缩短设计周期。研讨会包含四大主题：（1）面向物理综合与可制造性设计（DFM）的人工智能，探讨物理制造过程中的挑战及潜在的人工智能应用；（2）面向高层次与逻辑级综合（HLS/LLS）的人工智能，涵盖编译指示插入、程序转换、RTL代码生成等；（3）用于优化与设计的人工智能工具箱，讨论可能应用于EDA任务的前沿人工智能进展；（4）面向测试与验证的人工智能，包括LLM辅助验证工具、机器学习增强的SAT求解、安全性与可靠性挑战等。报告建议NSF应促进人工智能与EDA领域的协作，投资于EDA基础性人工智能研究，发展稳健的数据基础设施，推广可扩展的计算基础设施，并投资于人才培养，以普及硬件设计能力并赋能下一代硬件系统。研讨会详细信息可访问网站 https://ai4eda-workshop.github.io/ 获取。