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.

翻译：本文全面综述了机器学习（ML）算法在硬件加速器中的加速实现，重点关注现场可编程门阵列（FPGA）。研究回顾了过去六年中来自四大顶级FPGA会议的1138篇论文中的287篇。这一筛选突显了ML与FPGA技术日益紧密的融合及其在技术进步中的相互重要性。研究明确显示，相较于训练加速（13%），推理加速（81%）受到更多关注。此外，研究发现CNN主导了当前FPGA加速研究，而GNN等新兴模型呈现出显著的增长趋势。对FPGA研究论文的分类揭示了广泛的研究主题，证明了ML在FPGA研究中的相关性日益增强。本综合分析为ML应用背景下FPGA研究的当前趋势与未来方向提供了有价值的见解。