发表机构
The Chinese University of Hong Kong（香港中文大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SynAct是一种自适应闭环LLM推理-行动智能体，通过结合电路状态、工具知识与历史经验发布针对性命令，在14个商用设计实验中将平均WNS降至引导式综合的27%，实现高效自适应逻辑综合优化。
AI 中文摘要
逻辑综合将RTL设计转化为门级网表，其PPA结果高度依赖优化命令的选择，这使得综合调优既具有高维性又成本高昂。现有方法分为两类：自动化方法在固定动作空间上执行黑盒搜索，决策级可解释性有限；基于LLM的方法通常预先生成静态脚本，无法适应不断变化的电路状态。我们提出SynAct，一种自适应闭环LLM推理-行动智能体，它迭代诊断实时综合报告，并结合当前电路状态、检索到的工具知识和历史优化经验来发布针对性命令。SynAct专注于改善时序，尤其是最坏负松弛（WNS），同时保持面积和功耗的平衡权衡。在商用综合工具上对14个设计进行的实验表明，SynAct将平均WNS降低至引导式综合的27%。
英文摘要
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.
Comments12 pages, 8 figures
