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The remarkable capabilities and intricate nature of Artificial Intelligence(AI) have dramatically escalated the imperative for specialized AIaccelerators. Nonetheless, designing these accelerators for various AIworkloads remains both labor- and time-intensive. While existing designexploration and automation tools can partially alleviate the need for extensivehuman involvement, they still demand substantial hardware expertise, posing abarrier to non-experts and stifling AI accelerator development. Motivated bythe astonishing potential of large language models (LLMs) for generatinghigh-quality content in response to human language instructions, we embark onthis work to examine the possibility of harnessing LLMs to automate AIaccelerator design. Through this endeavor, we develop GPT4AIGChip, a frameworkintended to democratize AI accelerator design by leveraging human naturallanguages instead of domain-specific languages. Specifically, we first performan in-depth investigation into LLMs' limitations and capabilities for AIaccelerator design, thus aiding our understanding of our current position andgarnering insights into LLM-powered automated AI accelerator design.Furthermore, drawing inspiration from the above insights, we develop aframework called GPT4AIGChip, which features an automated demo-augmentedprompt-generation pipeline utilizing in-context learning to guide LLMs towardscreating high-quality AI accelerator design. To our knowledge, this work is thefirst to demonstrate an effective pipeline for LLM-powered automated AIaccelerator generation. Accordingly, we anticipate that our insights andframework can serve as a catalyst for innovations in next-generationLLM-powered design automation tools.

GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models