  Spiking Neural Networks (SNNs) are a promising alternative to traditional
deep learning methods since they perform event-driven information processing.
However, a major drawback of SNNs is high inference latency. The efficiency of
SNNs could be enhanced using compression methods such as pruning and
quantization. Notably, SNNs, unlike their non-spiking counterparts, consist of
a temporal dimension, the compression of which can lead to latency reduction.
In this paper, we propose spatial and temporal pruning of SNNs. First,
structured spatial pruning is performed by determining the layer-wise
significant dimensions using principal component analysis of the average
accumulated membrane potential of the neurons. This step leads to 10-14X model
compression. Additionally, it enables inference with lower latency and
decreases the spike count per inference. To further reduce latency, temporal
pruning is performed by gradually reducing the timesteps while training. The
networks are trained using surrogate gradient descent based backpropagation and
we validate the results on CIFAR10 and CIFAR100, using VGG architectures. The
spatiotemporally pruned SNNs achieve 89.04% and 66.4% accuracy on CIFAR10 and
CIFAR100, respectively, while performing inference with 3-30X reduced latency
compared to state-of-the-art SNNs. Moreover, they require 8-14X lesser compute
energy compared to their unpruned standard deep learning counterparts. The
energy numbers are obtained by multiplying the number of operations with energy
per operation. These SNNs also provide 1-4% higher robustness against Gaussian
noise corrupted inputs. Furthermore, we perform weight quantization and find
that performance remains reasonably stable up to 5-bit quantization.
