  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.
