AgenticPD: A Stage-Aware Agentic Framework for Closed-Loop Physical Design Optimization
Physical design quality-of-results (QoR) optimization is hard and expensive. Choices made at one stage can help or hurt later stages. Each evaluation requires a costly EDA run through the full flow. While existing methods still treat optimization as flat parameter tuning or a LLM-based script generation task, we present AgenticPD, a stage-aware agentic framework for physical design QoR optimization. Instead of re-running the full flow after every trial, AgenticPD is organized around the stage boundaries of the physical design flow, where a Judge Agent navigates the search and stage-specialized agents make local decisions within their own stage using stage-local tools. Additionally, the ag...
| Authors | Shuo Ren, Zijin Cheng, Yaohui Han et al. |
|---|---|
| Primary category | cs.AI |
| Published | 6 July 2026 |
| Last updated at source | 4 October 2026 |
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