Due to the ability to implement customized topology, FPGA is increasingly used to deploy SNNs in both embedded and high-performance applications. In this paper, we survey state-of-the-art SNN implementations and their applications on FPGA. We collect the recent widely-used spiking neuron models, network structures, and signal encoding formats, followed by the enumeration of related hardware design schemes for FPGA-based SNN implementations. Compared with the previous surveys, this manuscript enumerates the application instances that applied the above-mentioned technical schemes in recent research. Based on that, we discuss the actual acceleration potential of implementing SNN on FPGA. According to our above discussion, the upcoming trends are discussed in this paper and give a guideline for further advancement in related subjects.

翻译：由于FPGA具备定制化拓扑结构的能力，其在嵌入式和高性能应用中部署脉冲神经网络（SNN）的应用日益广泛。本文综述了当前最先进的SNN实现方案及其在FPGA上的应用。我们整理了近期广泛使用的脉冲神经元模型、网络结构和信号编码格式，并列举了基于FPGA的SNN实现相关的硬件设计方案。与以往综述相比，本文详细列举了近年来研究中应用上述技术方案的具体实例。在此基础上，我们讨论了在FPGA上实现SNN的实际加速潜力。根据上述讨论，本文还展望了未来发展趋势，并为相关领域的进一步研究提供了指导方向。