From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911570524635136 |
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| author | Ye, Junhao Hu, Yuchen Xu, Ke Pan, Dingrong Chen, Qichun Zhou, Jie Zhao, Shuai Fang, Xinwei Wang, Xi Guan, Nan Jiang, Zhe |
| author_facet | Ye, Junhao Hu, Yuchen Xu, Ke Pan, Dingrong Chen, Qichun Zhou, Jie Zhao, Shuai Fang, Xinwei Wang, Xi Guan, Nan Jiang, Zhe |
| contents | Verification presents a major bottleneck in Integrated Circuit (IC) development, consuming nearly 70% of the total development effort. While the Universal Verification Methodology (UVM) is widely used in industry to improve verification efficiency through structured and reusable testbenches, constructing these testbenches and generating sufficient stimuli remain challenging. These challenges arise from the considerable manual coding effort required, repetitive manual execution of multiple EDA tools, and the need for in-depth domain expertise to navigate complex designs.Here, we present UVM^2, an automated verification framework that leverages Large Language Models (LLMs) to generate UVM testbenches and iteratively refine them using coverage feedback, significantly reducing manual effort while maintaining rigorous verification standards.To evaluate UVM^2, we introduce a benchmark suite comprising Register Transfer Level (RTL) designs of up to 1.6K lines of code.The results show that UVM^2 reduces testbench setup time by up to UVM^2 compared to experienced engineers, and achieve average code and function coverage of 87.44% and 89.58%, outperforming state-of-the-art solutions by 20.96% and 23.51%, respectively. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_19959 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification Ye, Junhao Hu, Yuchen Xu, Ke Pan, Dingrong Chen, Qichun Zhou, Jie Zhao, Shuai Fang, Xinwei Wang, Xi Guan, Nan Jiang, Zhe Hardware Architecture Artificial Intelligence Verification presents a major bottleneck in Integrated Circuit (IC) development, consuming nearly 70% of the total development effort. While the Universal Verification Methodology (UVM) is widely used in industry to improve verification efficiency through structured and reusable testbenches, constructing these testbenches and generating sufficient stimuli remain challenging. These challenges arise from the considerable manual coding effort required, repetitive manual execution of multiple EDA tools, and the need for in-depth domain expertise to navigate complex designs.Here, we present UVM^2, an automated verification framework that leverages Large Language Models (LLMs) to generate UVM testbenches and iteratively refine them using coverage feedback, significantly reducing manual effort while maintaining rigorous verification standards.To evaluate UVM^2, we introduce a benchmark suite comprising Register Transfer Level (RTL) designs of up to 1.6K lines of code.The results show that UVM^2 reduces testbench setup time by up to UVM^2 compared to experienced engineers, and achieve average code and function coverage of 87.44% and 89.58%, outperforming state-of-the-art solutions by 20.96% and 23.51%, respectively. |
| title | From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification |
| topic | Hardware Architecture Artificial Intelligence |
| url | https://arxiv.org/abs/2504.19959 |