Automated QoR improvement in OpenROAD with coding agents

TL;DR

This paper introduces AuDoPEDA, an autonomous system using large language models to improve EDA tools like OpenROAD, achieving significant reductions in wirelength, clock period, and power with minimal human input.

Contribution

It presents a novel closed-loop LLM framework for EDA code improvements, a task suite for evaluation, and demonstrates effective automated optimization in OpenROAD.

Findings

Wirelength reduced by up to 5.9%

Clock period improved by up to 10.0%

Power consumption decreased by up to 19.4%

Abstract

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%.