LLM4SecHW: Leveraging Domain-Specific Large Language Model for Hardware Debugging
LLM4SecHW: Leveraging Domain-Specific Large Language Model for Hardware Debugging
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DOI：
10.1109/asianhost59942.2023.10409307
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发表时间：
2023-12
期刊：
影响因子：
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通讯作者：
Weimin Fu;Kaichen Yang;R. Dutta;Xiaolong Guo;Gang Qu
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文献类型：
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作者：
Weimin Fu;Kaichen Yang;R. Dutta;Xiaolong Guo;Gang Qu
本文介绍了LLM4SECHW，一种新的硬件调试框架，利用特定领域的大语言模型（LLM）。尽管LLM在自动化各种软件开发任务方面取得了成功，但由于商业LLM的限制和特定领域数据的稀缺，它们在硬件安全领域的应用受到限制。为了应对这些挑战，我们提出了一种独特的方法来编译开源硬件设计缺陷及其补救步骤的数据集，利用版本控制数据。该数据集为训练硬件机器学习模型提供了坚实的基础。LLM4SECHW基于该数据集对中型LLM进行微调，从而能够识别和纠正硬件设计中的错误。这种开创性的方法为其他研究领域中微调特定领域LLM的应用提供了参考工作流程。我们评估了我们提出的系统在各种开源硬件设计上的性能，证明了它在准确识别和纠正缺陷方面的有效性。我们的工作为硬件设计中的质量控制过程自动化带来了新的视角。
This paper presents LLM4SECHW, a novel framework for hardware debugging that leverages domain-specific Large Language Model (LLM). Despite the success of LLMs in automating various software development tasks, their application in the hardware security domain has been limited due to the constraints of commercial LLMs and the scarcity of domain-specific data. To address these challenges, we propose a unique approach to compile a dataset of open-source hardware design defects and their remediation steps, utilizing version control data. This dataset provides a substantial foundation for training machine learning models for hardware. LLM4SECHW employs fine-tuning of medium-sized LLMs based on this dataset, enabling the identification and rectification of bugs in hardware designs. This pioneering approach offers a reference workflow for the application of fine-tuning domain-specific LLMs in other research areas. We evaluate the performance of our proposed system on various open-source hardware designs, demonstrating its efficacy in accurately identifying and correcting defects. Our work brings a new perspective on automating the quality control process in hardware design.