Enhancing chip design verification through AI-powered bug detection in RTL code

This paper presents a novel AI-driven approach for enhancing chip design verification through automated bug detection in Register Transfer Level (RTL) code. The proposed method integrates advanced machine learning techniques with domain-specific knowledge of chip design to address the challenges of increasing complexity and time-to-market pressures in modern integrated circuit development. Our system employs a comprehensive data preprocessing pipeline that effectively captures syntactic and semantic features of RTL code, feeding into an innovative attention-based neural network model. The model demonstrates superior bug detection accuracy across diverse design categories and bug types compared to traditional methods and existing AI-assisted approaches. Extensive experimental evaluation on a large-scale dataset of RTL designs, including both open-source projects and industry collaborations, validates the effectiveness of our method. The proposed approach achieves a 95% accuracy in bug detection, with a 28-35% reduction in verification time when applied to real-world chip design projects. The paper addresses the interpretability of AI decisions in the context of chip design, presenting novel visualization techniques that enhance trust and facilitate adoption among RTL designers. While acknowledging current limitations, we discuss future research directions, including integration with formal verification methods and extension to system-level verification scenarios. This work contributes significantly to AI-assisted chip design, paving the way for more efficient and reliable development of complex integrated circuits.