用对比学习自动生成电路优化规则，提升性能功耗面积效率
AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning
- 通过代码对比抽象规则，无需人工预设优化经验
- 在多个基准电路上优于手动优化和现有最先进方法
- 适合芯片设计自动化方向的研究者与工程师
性能、功耗和面积（PPA）优化是RTL设计中的核心任务，需要精确理解电路功能及其结构与PPA指标之间的关系。现有研究尝试使用大语言模型（LLM）实现自动化，但反馈式或知识驱动的方法效率不足：前者缺乏先验知识，后者过度依赖人工总结的优化规则。本文提出AutoPPA，一种全自动化PPA优化框架。其核心思想是自动生成可增强最优解搜索的优化规则。为此，AutoPPA采用探索-评估-归纳（E²I）工作流，从多样化的生成代码对中对比并抽象规则，而非依赖人工定义的先验知识，从而获得更优的优化模式。为提高抽象规则的泛化能力，AutoPPA引入自适应多步搜索框架，针对特定电路选用最有效的规则。实验表明，AutoPPA在多个基准电路上优于手动优化及当前最先进的SymRTLO和RTLRewriter方法。
原文摘要 · Abstract (English)
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.
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