LLM 驱动的数据手册感知硬件兼容性自动验证框架，面向早期原理图前的嵌入式系统设计
LLM-Driven, Datasheet-Aware Automated Hardware Compatibility Verification for Early-Stage, Pre-Schematic Embedded System Design
AI 导读
一项 arXiv 研究提出由 LLM 驱动、基于数据手册的硬件兼容性验证框架，可在详细原理图仿真之前，依据硬件数据手册和高层组件连接描述识别文档级接口不兼容。框架将兼容性分析拆分为可追溯的模块化阶段，构建设计图并生成确定性脚本，避免依赖 LLM 做数值计算。在包含 34 份数据手册的 7 个嵌入式系统设计上，验证准确率达 97.5%，输入上下文规模较"上传后提问"流程减少 8.6 倍。
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