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

Yonggan Fu, Yongan Zhang, Zhongzhi Yu, Sixu Li, Zhifan Ye, Chaojian Li, Cheng Wan, Yingyan Celine Lin

TL;DR

This paper addresses the barrier to AI accelerator development by exploring whether large language models can automate accelerator design using natural language instructions. It introduces GPT4AIGChip, a framework that combines an LLM-friendly, modular hardware template with a demo-augmented prompt generator to harness in-context learning for hardware code generation. Through a comprehensive assessment of LLM capabilities and extensive experiments on a ZCU104 platform, the approach outperforms industry baselines and rivals manually optimized designs while reducing human effort. The work demonstrates a practical path toward LLM-driven design automation with potential to accelerate next-generation AI hardware development, while acknowledging limitations and outlining concrete avenues for future enhancement.

Abstract

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.

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

TL;DR

This paper addresses the barrier to AI accelerator development by exploring whether large language models can automate accelerator design using natural language instructions. It introduces GPT4AIGChip, a framework that combines an LLM-friendly, modular hardware template with a demo-augmented prompt generator to harness in-context learning for hardware code generation. Through a comprehensive assessment of LLM capabilities and extensive experiments on a ZCU104 platform, the approach outperforms industry baselines and rivals manually optimized designs while reducing human effort. The work demonstrates a practical path toward LLM-driven design automation with potential to accelerate next-generation AI hardware development, while acknowledging limitations and outlining concrete avenues for future enhancement.

Abstract

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.
Paper Structure (21 sections, 8 figures, 4 tables)

This paper contains 21 sections, 8 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Where We Are: The Limitations and Capabilities of Current LLMs for AI Accelerator Design
  3. An Overview of the Assessment
  4. Failures and Limitations of Existing LLMs
  5. Closed-sourced LLMs vs. Finetuned Open-sourced LLMs
  6. Capabilities of LLMs
  7. What We Can Learn: Insights from the Assessment
  8. Instantiate the Drawn Insights: The Proposed GPT4AIGChip Framework
  9. The Overall Pipeline of GPT4AIGChip
  10. The LLM-friendly Hardware Template Design
  11. The Demo-Augmented Prompt Generator Design
  12. Implementation of Other Components in GPT4AIGChip
  13. Experimental Evaluation
  14. Experiment Setup
  15. Benchmark with Manual and Automated Designs
  16. ...and 6 more sections

Figures (8)

  • Figure 1: A generic LLM-powered AI accelerator design automation pipeline.
  • Figure 2: Visualization of the identified common failures and limitations of existing LLMs for AI accelerator design automation.
  • Figure 3: Visualization of the identified generalization and logical reasoning capabilities of existing LLMs for AI accelerator design automation.
  • Figure 4: Visualize the workflow of our proposed GPT4AIGChip framework.
  • Figure 5: (a) LLMs with a non-modular template are limited by one-shot design generation, coupled design parameters, and long dependency; (b) In contrast, the proposed modular and decoupled accelerator template, which facilitates step-by-step design generation in a hierarchical manner.
  • ...and 3 more figures