arXiv:2508.13257cs.ARcs.AI2025-08被引 1

用大模型分析电路代码时序违规,自动定位并修复问题

ViTAD: Timing Violation-Aware Debugging of RTL Code using Large Language Models

  • 构建信号时序依赖图,结合大模型分析违规路径
  • 在真实项目数据集上修复成功率73.68%,提升19.30%
  • 适合芯片设计工程师快速调试时序问题

在现代超大规模集成电路设计流程中,寄存器传输级(RTL)阶段是时序优化的关键环节。针对高速系统中微小的时序违规可能导致功能失效或系统崩溃的问题,传统优化高度依赖人工经验,需反复分析时序报告进行调试。本文提出ViTAD方法,通过解析Verilog代码与时序报告,构建信号时序依赖图(STDG),基于该图进行违规路径分析,并利用大语言模型(LLMs)推断违规根源。随后,从领域专用知识库中检索相关调试知识,生成定制化修复方案。为评估效果,基于开源项目构建了包含54个时序违规案例的数据集。实验结果表明,该方法修复成功率达73.68%,相较仅使用大模型的基线(54.38%)提升19.30%。

原文摘要 · Abstract (English)

In modern Very Large Scale Integrated (VLSI) circuit design flow, the Register-Transfer Level (RTL) stage presents a critical opportunity for timing optimization. Addressing timing violations at this early stage is essential, as modern systems demand higher speeds, where even minor timing violations can lead to functional failures or system crashes. However, traditional timing optimization heavily relies on manual expertise, requiring engineers to iteratively analyze timing reports and debug. To automate this process, this paper proposes ViTAD, a method that efficiently analyzes the root causes of timing violations and dynamically generates targeted repair strategies. Specifically, we first parse Verilog code and timing reports to construct a Signal Timing Dependency Graph (STDG). Based on the STDG, we perform violation path analysis and use large language models (LLMs) to infer the root causes of violations. Finally, by analyzing the causes of violations, we selectively retrieve relevant debugging knowledge from a domain-specific knowledge base to generate customized repair solutions. To evaluate the effectiveness of our method, we construct a timing violation dataset based on real-world open-source projects. This dataset contains 54 cases of violations. Experimental results show that our method achieves a 73.68% success rate in repairing timing violations, while the baseline using only LLM is 54.38%. Our method improves the success rate by 19.30%.

芯片设计大模型时序优化RTL

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