GenAI-Driven Approach to RISC-V Supply Chain Exploration
For stakeholders in semiconductor supply chains, this work offers a novel method to integrate multimodal data for risk assessment, though it is an incremental application of existing AI techniques to a specific domain.
This paper proposes an LLM-empowered workflow combining Vision-Language Models and Model-Driven Engineering to analyze RISC-V supply chains, creating knowledge graphs and enabling formal validation. Evaluated in RISC-V scenarios, it demonstrates effectiveness in generating actionable insights and enhancing transparency.
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.