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%, and effective clock period reductions of up to 10.0%.

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。尽管前沿大语言模型在代码生成与科学推理任务中已展现出卓越性能，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系统——一个基于OpenAI模型构建的自主化、代码库赋能的编程框架，该系统通过集成Codex级智能代理实现以下功能：解析OpenROAD平台代码、提出研究方向、将方案扩展为可执行步骤，并提交可直接运行的代码差分文件。本研究的核心贡献包括：（i）面向EDA代码迭代的闭环式大语言模型框架；（ii）基于OpenROAD平台的性能-功耗-面积优化任务集与评估体系；（iii）需极少量人工干预的端到端实证演示。在OpenROAD平台上的实验表明，该系统可实现布线长度最高降低5.9%，有效时钟周期最高缩减10.0%。