CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design

• URL: http://arxiv.org/abs/2411.08510v1
• Date: Wed, 13 Nov 2024 10:45:19 GMT
• Title: CorrectBench: Automatic Testbench Generation with Functional Self-Correction using LLMs for HDL Design
• Authors: Ruidi Qiu, Grace Li Zhang, Rolf Drechsler, Ulf Schlichtmann, Bing Li,
• Abstract summary: We propose CorrectBench, an automatic testbench generation framework with functional self-validation and self-correction. The proposed approach can validate the correctness of the generated testbenches with a success rate of 88.85%. Our work's performance is 62.18% higher than previous work in sequential tasks and almost 5 times the pass ratio of the direct method.
• Score: 6.414167153186868
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Functional simulation is an essential step in digital hardware design. Recently, there has been a growing interest in leveraging Large Language Models (LLMs) for hardware testbench generation tasks. However, the inherent instability associated with LLMs often leads to functional errors in the generated testbenches. Previous methods do not incorporate automatic functional correction mechanisms without human intervention and still suffer from low success rates, especially for sequential tasks. To address this issue, we propose CorrectBench, an automatic testbench generation framework with functional self-validation and self-correction. Utilizing only the RTL specification in natural language, the proposed approach can validate the correctness of the generated testbenches with a success rate of 88.85%. Furthermore, the proposed LLM-based corrector employs bug information obtained during the self-validation process to perform functional self-correction on the generated testbenches. The comparative analysis demonstrates that our method achieves a pass ratio of 70.13% across all evaluated tasks, compared with the previous LLM-based testbench generation framework's 52.18% and a direct LLM-based generation method's 33.33%. Specifically in sequential circuits, our work's performance is 62.18% higher than previous work in sequential tasks and almost 5 times the pass ratio of the direct method. The codes and experimental results are open-sourced at the link: https://github.com/AutoBench/CorrectBench

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