GenAI-Driven Approach to RISC-V Supply Chain Exploration
The paper presents a novel approach to RISC-V supply chain analysis using GenAI technologies. It integrates Vision-Language Models and Model-Driven Engineering to provide insights from heterogeneous data sources. The methodology enhances decision-making and transparency in semiconductor supply chains through a knowledge graph and interactive validation mechanisms.
- ▪The proposed workflow leverages LLMs for textual understanding and VLMs for visual data extraction.
- ▪It addresses challenges posed by unstructured supply chain data by organizing information into a knowledge graph.
- ▪The approach is evaluated within RISC-V ecosystem scenarios, demonstrating its effectiveness in generating actionable insights.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15223 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary source text | contentText |
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| Cluster | 8Bz9mGv09g7h |
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| Publisher visit | Yes — open original |
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| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Hardware Architecture arXiv:2605.15223 (cs) [Submitted on 13 May 2026] Title:GenAI-Driven Approach to RISC-V Supply Chain Exploration Authors:Nenad Petrovic, Andre Schamschurko, Yingjie Xu, Alois Knoll View a PDF of the paper titled GenAI-Driven Approach to RISC-V Supply Chain Exploration, by Nenad Petrovic and 3 other authors View PDF HTML (experimental) Abstract:This paper presents an LLM-empowered workflow for RISC-V supply chain analysis, integrating Vision-Language Models (VLMs) and Model-Driven Engineering (MDE) to enable comprehensive, multimodal data-driven insights.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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