As the ubiquity and complexity of system-on-chip (SoC) designs increase across electronic devices, the task of incorporating security into an SoC design flow poses significant challenges. Existing security solutions are inadequate to provide effective verification of modern SoC designs due to their limitations in scalability, comprehensiveness, and adaptability. On the other hand, Large Language Models (LLMs) are celebrated for their remarkable success in natural language understanding, advanced reasoning, and program synthesis tasks. Recognizing an opportunity, our research delves into leveraging the emergent capabilities of Generative Pre-trained Transformers (GPTs) to address the existing gaps in SoC security, aiming for a more efficient, scalable, and adaptable methodology. By integrating LLMs into the SoC security verification paradigm, we open a new frontier of possibilities and challenges to ensure the security of increasingly complex SoCs. This paper offers an in-depth analysis of existing works, showcases practical case studies, demonstrates comprehensive experiments, and provides useful promoting guidelines. We also present the achievements, prospects, and challenges of employing LLM in different SoC security verification tasks.

翻译：随着电子设备中系统芯片（SoC）设计的普遍性与复杂性日益增加，将安全性融入SoC设计流程面临着重大挑战。现有安全解决方案在可扩展性、全面性和适应性方面存在局限性，难以对现代SoC设计提供有效验证。另一方面，大型语言模型（LLMs）因其在自然语言理解、高级推理和程序合成任务中的显著成功而备受瞩目。敏锐洞察到这一机遇，我们的研究深入探索利用生成式预训练变换器（GPTs）的新兴能力来填补SoC安全领域的现有空白，旨在构建更高效、可扩展且适应性强的安全验证方法论。通过将LLMs集成到SoC安全验证范式中，我们开启了确保日益复杂的SoC安全性的新前沿，既带来无限可能也伴随诸多挑战。本文通过深入分析现有成果、展示实际案例、开展全面实验并提供实用指导方针，系统阐述了LLM在不同SoC安全验证任务中的应用、成果、前景与挑战。