SynAct: A Reasoning-Acting Large Language Model Agent for Adaptive Synthesis Optimization
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 opt...
| Authors | Fangzhou Liu, Peiyi Han, Jiawei Liu et al. |
|---|---|
| Primary category | cs.AR |
| Published | 13 August 2026 |
| Last updated at source | 14 August 2026 |
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