The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have dramatically escalated the imperative for specialized AI accelerators. Nonetheless, designing these accelerators for various AI workloads remains both labor- and time-intensive. While existing design exploration and automation tools can partially alleviate the need for extensive human involvement, they still demand substantial hardware expertise, posing a barrier to non-experts and stifling AI accelerator development. Motivated by the astonishing potential of large language models (LLMs) for generating high-quality content in response to human language instructions, we embark on this work to examine the possibility of harnessing LLMs to automate AI accelerator design. Through this endeavor, we develop GPT4AIGChip, a framework intended to democratize AI accelerator design by leveraging human natural languages instead of domain-specific languages. Specifically, we first perform an in-depth investigation into LLMs' limitations and capabilities for AI accelerator design, thus aiding our understanding of our current position and garnering insights into LLM-powered automated AI accelerator design. Furthermore, drawing inspiration from the above insights, we develop a framework called GPT4AIGChip, which features an automated demo-augmented prompt-generation pipeline utilizing in-context learning to guide LLMs towards creating high-quality AI accelerator design. To our knowledge, this work is the first to demonstrate an effective pipeline for LLM-powered automated AI accelerator generation. Accordingly, we anticipate that our insights and framework can serve as a catalyst for innovations in next-generation LLM-powered design automation tools.

翻译：摘要：人工智能（AI）的卓越能力与复杂特性极大地提升了对专用AI加速器的需求。然而，针对不同AI工作负载设计此类加速器仍需耗费大量人力和时间。尽管现有的设计探索与自动化工具可在一定程度上减少对人工参与的依赖，但它们仍要求使用者具备深厚的硬件领域知识，这为非专业群体设置了障碍，并制约了AI加速器的发展。受大语言模型（LLMs）根据人类语言指令生成高质量内容惊人潜力的启发，本文着手探索利用LLMs实现AI加速器设计自动化的可行性。为此，我们开发了GPT4AIGChip框架，旨在通过利用人类自然语言而非领域特定语言，推动AI加速器设计的普及化。具体而言，我们首先深入研究了LLMs在AI加速器设计中的局限与能力，从而明确当前技术定位，并提炼出关于LLM驱动的自动化AI加速器设计的洞见。进一步地，基于上述洞见，我们开发了名为GPT4AIGChip的框架，该框架采用基于示例增强的自动提示生成流水线，通过上下文学习引导LLMs生成高质量的AI加速器设计。据我们所知，本文首次展示了基于LLM的自动化AI加速器生成的有效流水线。我们预期，本研究的洞见与框架将催化下一代基于LLM的设计自动化工具创新。