A Knowledge Graph-Based AI Framework for Managing Functional Validation Complexity in Next-Generation SoC Platforms

Abstract

The increasing complexity of next-generation System-on-Chip (SoC) platforms poses significant challenges in functional validation. Traditional approaches struggle with managing the expanding validation coverage, data volume, and integration scenarios. This paper proposes a novel AI-driven framework leveraging knowledge graphs (KGs) to systematically manage validation complexity by organizing heterogeneous validation data, capturing interdependencies, and enabling intelligent automation in test planning and bug localization. Our experiments show that integrating KGs can improve validation efficiency by 35% compared to conventional methods. The results demonstrate that this approach enhances traceability, boosts decision-making, and reduces the overall effort required in functional validation.