发表机构
Chair of Electronic Design Automation, Technical University of Munich; Resource-Efficient AI Group, Technical University of Ilmenau（电子设计自动化教授团，慕尼黑技术大学; 高效人工智能小组，伊门豪技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
探讨芯片前端设计瓶颈下，大语言模型在电子设计自动化中的潜力，回顾其在前端设计关键任务及流程中的进展，讨论集成大语言模型到EDA面临的挑战与限制，概述未来机遇，为相关研究提供系统视角。
AI 中文摘要
随着芯片复杂度增加和上市时间压力增大，前端设计成为芯片开发关键瓶颈。近期，大语言模型在电子设计自动化中展现巨大潜力，不仅能理解规范，还能作为统一智能接口用于硬件描述语言生成、测试平台构建及设计空间探索。以OpenClaw等为代表的智能AI兴起为下一代EDA提供战略路线图。本文探讨了EDA从局部辅助到自主智能执行的演变，回顾大语言模型在前端设计的代表性进展，聚焦从共享规范生成电路和测试平台等关键任务以及在高级综合等既定工作流程中提高设计质量。最后讨论将大语言模型集成到EDA中的关键挑战和限制，并概述推进基于大语言模型的前端设计的未来机遇，为利用智能AI技术进行EDA的研究人员提供系统视角。
英文摘要
As chip complexity increases and time-to-market pressures grow, front-end design has become a critical bottleneck in chip development. Recently, Large Language Models (LLMs) have shown great potential in Electronic Design Automation (EDA). Beyond specification understanding, LLMs show the potential to serve as a unified intelligent interface for hardware description language (HDL) generation, testbench construction, and design space exploration. The rise of agentic AI, represented by pioneering systems such as OpenClaw, offers a strategic roadmap for the next generation EDA. From this perspective, this paper discusses the evolution of EDA from localized assistance to autonomous agentic execution. Then, we review representative advances of LLMs in front-end design, focusing on key tasks such as circuit and testbench generation from a shared specification, as well as design quality improvement in established workflows such as high-level synthesis. Finally, we discuss the key challenges and limitations of integrating LLMs into EDA, and outline future opportunities for advancing LLM-enabled front-end design, offering a systematic perspective for researchers interested in leveraging agentic AI technologies for EDA.
CommentsInvited paper at the ACM/IEEE DAC 2026 Special Research Session, 5 pages, 9 figures
