LLM-Driven, Datasheet-Aware Automated Hardware Compatibility Verification for Early-Stage, Pre-Schematic Embedded System Design
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 16 references that are in the map. First posted on arXiv 25 Aug 2026.
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References · 7
- 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
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- 2020DocVQA: A Dataset for VQA on Document Imagescited by 1,317
- 2025Context Length Alone Hurts LLM Performance Despite Perfect Retrievalcited by 127
- 2025User Prompting Strategies and ChatGPT Contextual Adaptation Shape Conversational Information-Seeking Experiencescited by 2
- 2026DRCY: Agentic Hardware Design Reviewscited by 2
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