GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
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DOI：
10.1109/iccad57390.2023.10323953
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Yonggan Fu;Yongan Zhang;Zhongzhi Yu;Sixu Li;Zhifan Ye;Chaojian Li;Cheng Wan;Ying Lin
中科院分区：
文献类型：
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作者：
Yonggan Fu;Yongan Zhang;Zhongzhi Yu;Sixu Li;Zhifan Ye;Chaojian Li;Cheng Wan;Ying Lin
人工智能（AI）的卓越能力和复杂性质大大提升了专业AI加速器的必要性。尽管如此，为各种人工智能工作负载设计这些加速器仍然是劳动力和时间密集型的。虽然现有的设计探索和自动化工具可以部分缓解对大量人工参与的需求，但它们仍然需要大量的硬件专业知识，这对非专家构成了障碍，并扼杀了人工智能加速器的开发。受大型语言模型（LLM）在响应人类语言指令生成高质量内容方面的惊人潜力的启发，我们开始研究利用LLM自动化AI加速器设计的可能性。通过这一奋进，我们开发了GPT 4AIGChip，这是一个旨在通过利用人类自然语言而不是特定于领域的语言来实现人工智能加速器设计民主化的框架。具体来说，我们首先对LLM在AI加速器设计方面的局限性和能力进行了深入调查，从而帮助我们了解我们目前的地位，并深入了解LLM驱动的自动化AI加速器设计。此外，从上述见解中汲取灵感，我们开发了一个名为GPT4AIGChip的框架，该框架具有自动演示增强的数据生成管道，利用上下文学习来指导LLM创建高质量的AI加速器设计。据我们所知，这项工作是第一个展示LLM驱动的自动AI加速器生成的有效管道。因此，我们预计我们的见解和框架可以作为下一代LLM驱动的设计自动化工具创新的催化剂。
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.