综述FPGA加速机器学习的现状与趋势，聚焦推理优化与模型演进。
A survey on FPGA-based accelerator for ML models
- 系统梳理近六年287篇顶会论文，分析FPGA在ML中的应用方向。
- 81%研究聚焦推理加速，卷积网络（CNN）仍是主流，图神经网络（GNN）增长显著。
- 适合关注硬件加速、FPGA开发及机器学习部署的工程师和研究者。
本文全面综述了过去六年中基于现场可编程门阵列（FPGA）的机器学习（ML）算法硬件加速研究，涵盖来自四个顶级FPGA会议的1138篇论文中的287篇。研究显示，当前工作以推理加速为主（占81%），训练加速仅占13%。卷积神经网络（CNN）在现有FPGA加速研究中占据主导地位，而图神经网络（GNN）等新兴模型展现出明显增长趋势。论文分类揭示了该领域广泛的研究主题，体现了机器学习与FPGA技术融合日益加深。本综述为理解当前趋势与未来发展方向提供了重要参考。
原文摘要 · Abstract (English)
This paper thoroughly surveys machine learning (ML) algorithms acceleration in hardware accelerators, focusing on Field-Programmable Gate Arrays (FPGAs). It reviews 287 out of 1138 papers from the past six years, sourced from four top FPGA conferences. Such selection underscores the increasing integration of ML and FPGA technologies and their mutual importance in technological advancement. Research clearly emphasises inference acceleration (81\%) compared to training acceleration (13\%). Additionally, the findings reveals that CNN dominates current FPGA acceleration research while emerging models like GNN show obvious growth trends. The categorization of the FPGA research papers reveals a wide range of topics, demonstrating the growing relevance of ML in FPGA research. This comprehensive analysis provides valuable insights into the current trends and future directions of FPGA research in the context of ML applications.
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