让AI动态优化电路合成，显著改善时序表现。
SynAct: A Reasoning-Acting Large Language Model Agent for Adaptive Synthesis Optimization
- 基于推理-行动闭环机制，实时分析电路状态并生成指令
- 在14个设计上将最差负松弛降低至原始的27%
- 适合需要高效时序优化的芯片设计团队
逻辑综合将RTL设计转化为门级网表，其性能、功耗和面积（PPA）结果对优化命令选择极为敏感，导致综合调优具有高维性和高成本。现有方法分为两类：自动化方法在固定动作空间中进行黑盒搜索，缺乏决策可解释性；基于大模型的方法通常提前生成静态脚本，无法适应电路状态变化。本文提出SynAct，一种自适应闭环的LLM推理-行动代理，可迭代诊断实时综合报告，结合当前电路状态、检索到的工具知识和历史优化经验，生成精准优化指令。该方法聚焦于改善时序，尤其是最差负松弛（WNS），同时保持面积与功耗的平衡。在商用综合工具上对14个设计的实验表明，SynAct将平均WNS降低至初始综合结果的27%。
原文摘要 · Abstract (English)
Logic synthesis transforms RTL designs into gate-level netlists, where PPA results are highly sensitive to the choice of optimization commands, making synthesis tuning both high-dimensional and expensive. Previous approaches fall into two categories: automated methods, which perform black-box search over fixed action spaces with limited decision-level interpretability, and LLM-based methods, which typically generate static scripts upfront and cannot adapt to evolving circuit states. We present SynAct, an adaptive closed-loop LLM reasoning--acting agent that iteratively diagnoses live synthesis reports and reasons over the current circuit state, retrieved tool knowledge, and historical optimization experience to issue targeted commands. SynAct focuses on improving timing, particularly worst negative slack (WNS), while maintaining balanced area and power trade-offs. Experiments on a commercial synthesis tool across 14 designs show that SynAct reduces average WNS to 27% of that from bootstrap synthesis.
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