用大模型提升芯片设计效率，自动完成从设计到制造的全流程
Large Language Models (LLMs) for Electronic Design Automation (EDA)
- 将芯片设计流程文本化，用大模型理解并生成设计代码
- 在设计、测试、优化三类任务中验证了大模型的有效性
- 适合想用AI加速芯片开发的研究者和工程师
随着现代集成电路复杂度持续攀升，硬件工程师需投入更多精力于从设计到制造的全流程。该流程涉及大量迭代，既耗时又易出错。因此，亟需更高效的电子设计自动化（EDA）方案以加速硬件研发。近期，大语言模型（LLMs）在上下文理解、逻辑推理和生成能力方面取得显著进展。由于硬件设计及中间脚本可表示为文本，将LLM融入EDA有望简化甚至自动化整个流程。本文全面综述了LLM在EDA中的应用，重点分析其能力、局限与未来机遇。通过三个案例研究——硬件设计、测试与优化——展示了LLM的实际效能。最后，论文指出了未来方向与挑战，为希望利用先进AI技术推动下一代EDA的研究者提供重要参考。
原文摘要 · Abstract (English)
With the growing complexity of modern integrated circuits, hardware engineers are required to devote more effort to the full design-to-manufacturing workflow. This workflow involves numerous iterations, making it both labor-intensive and error-prone. Therefore, there is an urgent demand for more efficient Electronic Design Automation (EDA) solutions to accelerate hardware development. Recently, large language models (LLMs) have shown remarkable advancements in contextual comprehension, logical reasoning, and generative capabilities. Since hardware designs and intermediate scripts can be represented as text, integrating LLM for EDA offers a promising opportunity to simplify and even automate the entire workflow. Accordingly, this paper provides a comprehensive overview of incorporating LLMs into EDA, with emphasis on their capabilities, limitations, and future opportunities. Three case studies, along with their outlook, are introduced to demonstrate the capabilities of LLMs in hardware design, testing, and optimization. Finally, future directions and challenges are highlighted to further explore the potential of LLMs in shaping the next-generation EDA, providing valuable insights for researchers interested in leveraging advanced AI technologies for EDA.
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