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arXiv 2604.18445cs.LGcs.AR
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AutoPPA：通过对比代码基于的规则库学习实现自动电路PPA优化

AutoPPA: Automated Circuit PPA Optimization via Contrastive Code-based Rule Library Learning

State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China（中国科学院计算技术研究所处理器重点实验室，北京，中国）；University of Chinese Academy of Sciences, Beijing, China（中国科学院大学，北京，中国）；University of Science and Technology of China, Hefei, China（中国科学技术大学，合肥，中国）；Institute of AI for Industries, Chinese Academy of Sciences, Nanjing, China（中国科学院工业人工智能研究所，南京，中国）
Chongxiao Li, Pengwei Jin, Di Huang, Guangrun Sun, Husheng Han, Jianan Mu, Xinyao Zheng, Jiaguo Zhu, Shuyi Xing, Hanjun Wei, Tianyun Ma, Shuyao Cheng, Rui Zhang… 展开作者

Chongxiao Li, Pengwei Jin, Di Huang, Guangrun Sun, Husheng Han, Jianan Mu, Xinyao Zheng, Jiaguo Zhu, Shuyi Xing, Hanjun Wei, Tianyun Ma, Shuyao Cheng, Rui Zhang, Ying Wang, Zidong Du, Qi Guo, Xing Hu

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AI总结：

本文提出AutoPPA框架，通过对比生成的代码对自动生成优化规则，提升RTL设计中PPA优化效率，实验表明优于手动优化和现有方法SymRTLO和RTLRewriter。

摘要：

性能、功率和面积（PPA）优化是RTL设计中的基础任务，需要精确理解电路功能及电路结构与PPA指标的关系。近期研究尝试利用LLMs自动化此过程，但反馈或知识驱动的方法效率不足，要么缺乏先验知识，要么依赖人工总结的优化规则。本文提出AutoPPA，一个完全自动化的PPA优化框架。核心思想是自动生成优化规则以增强最优解的搜索。为此，AutoPPA采用探索-评估-诱导（E²I）工作流，通过对比和抽象不同生成代码对中的规则，而非手动定义的先验知识，从而获得更好的优化模式。为使抽象的规则更具通用性，AutoPPA采用适应性多步搜索框架，采用最适合给定电路的规则。实验表明，AutoPPA在手动优化和现有方法SymRTLO和RTLRewriter上均表现更优。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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