用AI自动优化芯片设计工具，显著提升性能功耗面积
Automated QoR improvement in OpenROAD with coding agents
- 基于大模型的自主代码生成系统，闭环改进开源芯片工具链
- 实现布线长度减少5.9%、时钟周期缩短10.0%、功耗降低19.4%
- 适合芯片EDA开发人员和自动化工具研究者参考
EDA开发与创新受限于高端工程人才短缺。尽管领先的大语言模型在编程和科学推理任务中表现优异，但其对EDA技术本身的推动力尚未充分验证。我们提出AuDoPEDA，一个基于OpenAI模型和Codex类代理的自主、仓库驱动的编码系统，可读取OpenROAD，提出研究方向，分解为实施步骤，并提交可执行的代码变更。贡献包括：(i) 面向EDA代码修改的闭环大模型框架；(ii) 针对PPA优化的OpenROAD任务集与评估协议；(iii) 低人工干预下的端到端演示。实验表明，OpenROAD中实现布线长度最多降低5.9%，有效时钟周期最多缩短10.0%，功耗最多降低19.4%。
原文摘要 · Abstract (English)
EDA development and innovation has been constrained by scarcity of expert engineering resources. While leading LLMs have demonstrated excellent performance in coding and scientific reasoning tasks, their capacity to advance EDA technology itself has been largely untested. We present AuDoPEDA, an autonomous, repository-grounded coding system built atop OpenAI models and a Codex-class agent that reads OpenROAD, proposes research directions, expands them into implementation steps, and submits executable diffs. Our contributions include (i) a closed-loop LLM framework for EDA code changes; (ii) a task suite and evaluation protocol on OpenROAD for PPA-oriented improvements; and (iii) end-to-end demonstrations with minimal human oversight. Experiments in OpenROAD achieve routed wirelength reductions of up to 5.9%, effective clock period reductions of up to 10.0%, and power reductions of up to 19.4%.
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