Authors: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
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Hardware Architecture (cs.AR) |
| Cite as: | arXiv:2604.18445 [cs.LG] |
| (or arXiv:2604.18445v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.18445 arXiv-issued DOI via DataCite |
From: Chongxiao Li [view email]
[v1]
Mon, 20 Apr 2026 16:02:04 UTC (394 KB)
[v2]
Tue, 28 Apr 2026 04:59:28 UTC (393 KB)
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