用大模型分析硬件设计缺陷根源，准确率超90%。
Towards LLM-based Root Cause Analysis of Hardware Design Failures
- 用大模型解析合成与仿真中的设计错误根源
- o3-mini模型在34个场景下准确率达100%（pass@5）
- 检索增强生成使多数模型性能超90%，适合硬件安全分析
随着大语言模型（LLMs）的发展，为数字硬件设计流程提供支持的新工具应运而生。本文探索了LLMs在解释综合与仿真阶段揭示的设计问题及漏洞根源方面的应用，这是推动LLMs广泛应用于硬件设计与硬件安全分析的关键一步。在包含34种不同错误场景的语料库中，OpenAI的o3-mini推理模型在pass@5评分下实现了100%的正确判定；其他先进模型和配置通常表现超过80%，在采用检索增强生成（RAG）时更是超过90%。
原文摘要 · Abstract (English)
With advances in large language models (LLMs), new opportunities have emerged to develop tools that support the digital hardware design process. In this work, we explore how LLMs can assist with explaining the root cause of design issues and bugs that are revealed during synthesis and simulation, a necessary milestone on the pathway towards widespread use of LLMs in the hardware design process and for hardware security analysis. We find promising results: for our corpus of 34 different buggy scenarios, OpenAI's o3-mini reasoning model reached a correct determination 100% of the time under pass@5 scoring, with other state of the art models and configurations usually achieving more than 80% performance and more than 90% when assisted with retrieval-augmented generation.
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