AI-Driven Design Verification of Semiconductor ICs for Graphics Processing Unit Using LLMs
Keywords:
Artificial Intelligence (AI), GPU Verification, Design Verification, Large Language Models (LLMs), Functional Verification, Formal Verification, Simulation-based Verification, AI-driven Verification, RTL Code Analysis, Deep Learning for VerificationAbstract
The exponential growth of next-generation GPU technologies, for gaming and now AI processing, demands highly reliable and efficient semiconductor chip designs. As chip complexity surges, traditional verification methodologies are increasingly challenged by limitations in scalability, time, and coverage. In this context, Artificial Intelligence (AI), particularly Large Language Models (LLMs), offers transformative potential in automating and accelerating the chip design verification process. This paper presents an AI-driven framework leveraging LLMs for the verification of semiconductor chips tailored for GPU systems. We explore how LLMs can interpret design specifications, generate test cases, identify anomalies, and assist in natural language debugging, thereby significantly enhancing verification throughput and accuracy. The proposed approach integrates LLMs with formal verification tools and simulation environments, enabling contextual understanding of hardware description languages (HDLs) and streamlining functional and system-level validation. Additionally, we examine case studies demonstrating improvements in error detection, coverage analysis, and design cycle reduction GPU components. Our findings show that LLM-assisted verification achieves notable gains in identifying logic bugs, reducing verification effort, and ensuring standards compliance in complex chip designs. We also discuss the challenges of domain adaptation, model fine-tuning for HDL context, and handling proprietary IP sensitivity. Finally, this research lays the groundwork for broader adoption of AI-augmented verification pipelines in semiconductor development for advanced communication technologies. The integration of LLMs into chip design workflows not only enhances productivity but also redefines the paradigm of intelligent design verification, aligning with the rapid pace of innovation in the GPU landscape.
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