Biologically-inspired Spiking Neural Networks (SNNs), processing information using discrete-time events known as spikes rather than continuous values, have garnered significant attention due to their hardware-friendly and energy-efficient characteristics. However, the training of SNNs necessitates a considerably large memory footprint, given the additional storage requirements for spikes or events, leading to a complex structure and dynamic setup. In this paper, to address memory constraint in SNN training, we introduce an innovative framework, characterized by a remarkably low memory footprint. We \textbf{(i)} design a reversible SNN node that retains a high level of accuracy. Our design is able to achieve a $\mathbf{58.65\times}$ reduction in memory usage compared to the current SNN node. We \textbf{(ii)} propose a unique algorithm to streamline the backpropagation process of our reversible SNN node. This significantly trims the backward Floating Point Operations Per Second (FLOPs), thereby accelerating the training process in comparison to current reversible layer backpropagation method. By using our algorithm, the training time is able to be curtailed by $\mathbf{23.8\%}$ relative to existing reversible layer architectures.

翻译：生物启发的脉冲神经网络（SNN）利用离散时间事件（即脉冲）而非连续值处理信息，因其硬件友好和节能特性而备受关注。然而，由于需要额外存储脉冲或事件，SNN的训练需消耗大量内存，导致结构复杂且动态设置繁琐。本文针对SNN训练中的内存限制问题，提出了一种内存占用极低的创新框架。我们（i）设计了一种保持高精度的可逆SNN节点，相比现有SNN节点，内存使用量减少了$\mathbf{58.65\times}$。（ii）提出了一种独特算法来简化可逆SNN节点的反向传播过程，大幅降低了反向浮点运算次数（FLOPs），从而相比现有可逆层反向传播方法加速了训练过程。采用该算法后，相比现有可逆层架构，训练时间可缩短$\mathbf{23.8\%}$。