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%.

翻译：电子设计自动化（EDA）领域的发展与创新长期受限于专家工程资源的稀缺性。尽管领先的大语言模型（LLM）已在编码与科学推理任务中展现出卓越性能，但其推动EDA技术本身发展的潜力尚未得到充分验证。本文提出AuDoPEDA——一个基于OpenAI模型构建的、以代码库为根基的自主编码系统，该系统采用Codex级智能体读取OpenROAD平台代码，提出研究方向，将其扩展为实施步骤，并提交可执行的代码变更。我们的核心贡献包括：（i）面向EDA代码变更的闭环LLM框架；（ii）针对OpenROAD平台、以性能-功耗-面积（PPA）为导向的改进任务集与评估方案；（iii）在最小人工监督下实现的端到端验证。在OpenROAD平台上的实验实现了布线长度最高降低5.9%，有效时钟周期最高缩减10.0%，功耗最高下降19.4%的优化效果。