arXiv cs.AI· Wenji Fang, Yao Lu, Jinghua Wang, Zhiyao Xie·· 10 小时前AI 评分38
面向自主 RTL 与综合优化：基准测试与自改进智能体
Toward Autonomous RTL and Synthesis Optimization: Benchmarking and Self-Improving Agents
AI 导读
研究提出 Dr. RTL 多智能体框架，自动完成关键路径分析、RTL 重写与实现级评估，并通过可复用优化技能持续自我改进。在 20 个真实 RTL 设计上，Dr. RTL 相比商用综合实现平均 WNS/TNS 提升 21%/17%，面积减少 6%。论文同时讨论基于参考与开放式的 RTL 优化基准，以及智能体驱动的综合脚本调优与可执行综合算法发现。
正文 · 原文
Abstract:Recent advances in large language models (LLMs) and autonomous agents are creating new opportunities for hardware design optimization. This paper examines three connected directions toward autonomous RTL and synthesis optimization: (1) Realistic benchmarking. We discuss reference-based and open-ended RTL optimization benchmarks that evaluate optimization opportunities under realistic EDA workflows. (2) Tool-grounded RTL optimization. We present Dr. RTL, a multi-agent framework that automates critical-path analysis, RTL rewriting, and implementation-level evaluation, while continuously improving through reusable optimization skills. Evaluated on 20 real-world RTL designs, Dr.~RTL achieves average WNS/TNS improvements of 21\%/17\% with a 6\% area reduction over commercial synthesis. (3) Design-specific synthesis optimization. We explore agent-driven synthesis script tuning and the discovery of executable synthesis algorithms across intermediate representations, technology-independent optimization, and technology mapping. Finally, we envision AI-native design automation that jointly explores architectural, RTL, and EDA algorithm design spaces, guided by verification constraints and implementation feedback.
| Comments: | ASP-DAC'27 Invited Paper |
| Subjects: | Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR) |
| Cite as: | arXiv:2611.13713 [cs.AI] |
| (or arXiv:2611.13713v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.13713 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Wenji Fang [view email]
[v1]
Tue, 10 Nov 2026 08:07:31 UTC (7,748 KB)
来源：arXiv cs.AI · arxiv.org