Performance, power, and area (PPA) optimization is a fundamental task in RTL design, requiring a precise understanding of circuit functionality and the relationship between circuit structures and PPA metrics. Recent studies attempt to automate this process using LLMs, but neither feedback-based nor knowledge-based methods are efficient enough, as they either design without any prior knowledge or rely heavily on human-summarized optimization rules. In this paper, we propose AutoPPA, a fully automated PPA optimization framework. The key idea is to automatically generate optimization rules that enhance the search for optimal solutions. To do this, AutoPPA employs an Explore-Evaluate-Induce ($E^2I$) workflow that contrasts and abstracts rules from diverse generated code pairs rather than manually defined prior knowledge, yielding better optimization patterns. To make the abstracted rules more generalizable, AutoPPA employs an adaptive multi-step search framework that adopts the most effective rules for a given circuit. Experiments show that AutoPPA outperforms both the manual optimization and the state-of-the-art methods SymRTLO and RTLRewriter.

翻译：性能、功耗和面积（PPA）优化是RTL设计中的基础任务，需要精确理解电路功能以及电路结构与PPA指标之间的关系。近年来的研究尝试利用大语言模型（LLMs）自动化这一过程，但无论是基于反馈的方法还是基于知识的方法效率均显不足，因为前者缺乏先验知识进行设计，而后者过度依赖人工总结的优化规则。本文提出AutoPPA——一个全自动PPA优化框架，其核心思想是通过自动生成优化规则来增强最优解的搜索效率。为实现这一目标，AutoPPA采用"探索-评估-归纳（Explore-Evaluate-Induce，E²I）"工作流，通过对比分析不同生成代码对中的差异并抽象规则，而非依赖人工定义的先验知识，从而获得更优的优化模式。为使抽象规则更具泛化能力，AutoPPA引入自适应多步搜索框架，为特定电路选取最有效的规则。实验表明，AutoPPA在优化效果上优于人工优化及当前最先进的SymRTLO和RTLRewriter方法。