Spiking Neural Networks (SNNs) can reduce energy consumption compared to conventional Artificial Neural Networks (ANNs) when spiking activity is sparse and the neuron model is hardware-friendly. However, biologically faithful models are often too costly to implement on FPGAs, whereas very simple models (e.g., IR/LIF) sacrifice part of the neuronal dynamics. In this work, we present an FPGA accelerator for an SNN using Spiking Recurrent Cell (SRC) neurons, providing a trade-off between biological plausibility and hardware cost. We propose a set of mathematical simplifications that remove costly unary operators (\textit{tanh}, \textit{exp}) and avoid floating-point arithmetic through scaling and piecewise-defined approximations. The complete network is implemented in VHDL and validated using spiking traces derived from the MNIST dataset. The weight matrices computed off-line are stored directly in LUT-registers without any adaptation. This demonstrates the robustness of SRC cells. Experiments were conducted on an Artix-7 XC7A200T clocked at 100 MHz. The reference implementation achieves 96.31\% accuracy with a 220-image spiking trace and a processing time of 1.7424 ms per digit. We then investigate accuracy/energy trade-offs by reducing the spiking trace length and quantizing synaptic weights down to 4 bits, achieving 93.32\% accuracy at 0.55 mJ per digit (55 images, 5-bit weights) and 92.89\% at 0.45 mJ (44 images, 4-bit weights). These results show that SRC-based SNNs can deliver competitive performance with reduced energy consumption, while preserving richer neuronal dynamics than standard LIF/IR models.

翻译：脉冲神经网络（SNN）相较于传统人工神经网络（ANN），在脉冲活动稀疏且神经元模型硬件友好时，可降低能耗。然而，生物逼真模型往往在FPGA上实现成本过高，而极简模型（如IR/LIF）会牺牲部分神经动力学特性。本文提出一种基于脉动循环细胞（SRC）神经元的SNN的FPGA加速器，在生物合理性及硬件成本间取得平衡。我们提出一组数学简化方法：移除计算成本高昂的一元运算符（tanh、exp），并通过缩放与分段定义的近似方法避免浮点运算。整个网络采用VHDL实现，并利用从MNIST数据集导出的脉冲迹验证。离线计算的权重矩阵直接存储于LUT寄存器中，无需任何自适应调整，这证明了SRC细胞的鲁棒性。实验在时钟频率为100 MHz的Artix-7 XC7A200T上进行。参考实现在220张图像的脉冲迹下达到96.31%的准确率，每字符处理时间为1.7424毫秒。随后通过缩短脉冲迹长度并将突触权重量化至4比特，研究精度/能耗权衡：在5比特权重下，以55张图像实现每字符0.55毫焦能耗、93.32%准确率；在4比特权重下，以44张图像实现每字符0.45毫焦能耗、92.89%准确率。结果表明，基于SRC的SNN在保持比标准LIF/IR模型更丰富的神经动力学特性的同时，能以较低能耗达到竞争性性能。