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.

翻译：本文提出了LLM4SecHW，一种新颖的硬件调试框架，该框架利用领域特定的大语言模型（LLM）。尽管LLM在自动化各类软件开发任务方面取得了成功，但由于商业LLM的限制以及领域特定数据的稀缺性，其在硬件安全领域的应用仍然有限。为应对这些挑战，我们提出了一种独特的方法，利用版本控制数据编译开源硬件设计缺陷及其修复步骤的数据集。该数据集为训练面向硬件的机器学习模型提供了坚实基础。LLM4SecHW基于该数据集对中等规模的LLM进行微调，从而能够识别和修复硬件设计中的错误。这一开创性方法为其他研究领域应用微调领域特定LLM提供了参考工作流。我们在多种开源硬件设计上评估了所提系统的性能，展示了其在准确识别和纠正缺陷方面的有效性。我们的工作为自动化硬件设计质量控制流程带来了全新视角。