arXiv cs.AI· Haotian Qiao, Robert P. Dick·· 14 小时前AI 评分29
基于 LLM 与数据手册的嵌入式系统早期硬件兼容性自动验证框架
LLM-Driven, Datasheet-Aware Automated Hardware Compatibility Verification for Early-Stage, Pre-Schematic Embedded System Design
AI 导读
研究者提出一个 LLM 驱动、基于数据手册的框架，可在原理图仿真之前验证硬件接口兼容性，在 7 个嵌入式设计、34 份数据手册上达到 97.5% 的验证准确率。该框架将兼容性分析拆分为模块化阶段并保留中间结果，输入上下文规模相比“上传即提问”流程缩小 8.6 倍，且数值计算不依赖 LLM。
正文 · 原文
Abstract:We present an LLM-driven, datasheet-aware framework for early-stage hardware compatibility verification that identifies documentation-level interface incompatibilities based on hardware datasheets and high-level component connectivity descriptions. It does not require, and can therefore be used, before detailed schematic simulation and implementation. We view trustworthy LLM-assisted design automation not as directly generating answers from documents, but as transforming engineering information through traceable verification stages. Given hardware datasheets and high-level component connectivity descriptions, the framework constructs a design graph that captures device connectivity and shared interaction domains, retrieves only the engineering properties required by explicit, domain-oriented verification criteria , and generates deterministic scripts for compatibility evaluation. By decomposing compatibility analysis into modular stages and preserving intermediate results, the framework reduces context overhead, improves transparency and tractability, enables scaling, and avoids reliance on LLMs for numerical computation. Evaluated on seven embedded-system designs comprising 34 datasheets, our framework achieves 97.5% compatibility-verification accuracy and an 8.6 times reduction in input context size compared with ``upload-and-query'' workflows. These results demonstrate the feasibility of LLM-assisted, specification-based hardware compatibility verification at an early design stage, as well as the need for, and substantial benefits of, modular task decomposition, formalized verification criteria, and task-aware compact context construction.
| Subjects: | Artificial Intelligence (cs.AI); Systems and Control (eess.SY) |
| Cite as: | arXiv:2608.25217 [cs.AI] |
| (or arXiv:2608.25217v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.25217 arXiv-issued DOI via DataCite |
Submission history
From: Haotian Qiao [view email]
[v1]
Tue, 25 Aug 2026 23:12:48 UTC (1,458 KB)
[v2]
Tue, 27 Oct 2026 21:10:26 UTC (1,079 KB)
来源：arXiv cs.AI · arxiv.org