  There is an increasing interest in emulating Spiking Neural Networks (SNNs)
on neuromorphic computing devices due to their low energy consumption. Recent
advances have allowed training SNNs to a point where they start to compete with
traditional Artificial Neural Networks (ANNs) in terms of accuracy, while at
the same time being energy efficient when run on neuromorphic hardware.
However, the process of training SNNs is still based on dense tensor operations
originally developed for ANNs which do not leverage the spatiotemporally sparse
nature of SNNs. We present here the first sparse SNN backpropagation algorithm
which achieves the same or better accuracy as current state of the art methods
while being significantly faster and more memory efficient. We show the
effectiveness of our method on real datasets of varying complexity
(Fashion-MNIST, Neuromophic-MNIST and Spiking Heidelberg Digits) achieving a
speedup in the backward pass of up to 150x, and 85% more memory efficient,
without losing accuracy.
