GenAI-Driven Approach to RISC-V Supply Chain Exploration

2026-05-13

AI Summary

This work addresses the challenges of information extraction and dependency analysis in the RISC-V supply chain, where heterogeneous and unstructured data impede transparency. To overcome these limitations, the authors propose a multimodal understanding framework that synergistically integrates large language models (LLMs), vision-language models (VLMs), and model-driven engineering (MDE). This approach uniquely combines LLMs and VLMs within the semiconductor supply chain context to extract entity relationships from both textual and visual sources, constructing a knowledge graph that supports formal verification and risk assessment through constraint-based modeling. The framework enables human-in-the-loop querying and provides actionable insights for complex decision-making. Experimental evaluation in the RISC-V ecosystem demonstrates that the proposed method significantly enhances supply chain visibility and effectively identifies bottlenecks and vulnerabilities.

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. The proposed approach addresses the challenges of heterogeneous and unstructured supply chain data by leveraging LLMs for textual understanding and VLMs for extracting information from visual artifacts such as diagrams, tables, and scanned documents. These models collaboratively identify key entities and relationships, which are then organized into a knowledge graph representing supply chain components and their interdependencies. For analytical reasoning, the workflow incorporates MDE techniques and constraint-based modeling to enable formal validation of dependencies, detection of bottlenecks, and assessment of risks. The synergy between LLM- and VLM-based semantic understanding and MDE-based formal analysis supports both exploratory and systematic evaluation of supply chain resilience. A human-in-the-loop mechanism further enables interactive querying and expert validation. The approach is evaluated in RISC-V ecosystem scenarios, demonstrating its effectiveness in generating actionable insights, enhancing transparency, and supporting decision-making in complex semiconductor supply chains.

Problem

• RISC-V
• supply chain analysis
• heterogeneous data
• multimodal data
• supply chain resilience

Innovation

• Large Language Models
• Vision-Language Models
• Model-Driven Engineering
• Knowledge Graph
• Supply Chain Resilience