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、功耗与面积（PPA）优化是RTL设计中的基础任务，要求精确理解电路功能以及电路结构与PPA指标之间的关系。近期研究尝试利用大语言模型自动化该过程，但基于反馈或知识的方法效率均有限，前者缺乏先验知识指导设计，后者则过度依赖人工总结的优化规则。本文提出AutoPPA——一种全自动PPA优化框架。其核心思想是自动生成能够增强最优解搜索效率的优化规则。为此，AutoPPA采用探索-评估-归纳（$E^2I$）工作流，通过对比分析不同生成代码对自动抽象规则，替代人工定义先验知识，从而获得更优的优化模式。为提升抽象规则的泛化能力，AutoPPA设计了自适应多步搜索框架，能够针对特定电路采纳最有效的规则。实验表明，AutoPPA在性能上优于人工优化及当前最先进的SymRTLO和RTLRewriter方法。