  Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of
the human brain and are highly energy-efficient, making them well-suited for
low-power edge devices. However, the pursuit of accuracy in current studies
leads to large, long-timestep SNNs, conflicting with the resource constraints
of these devices. In order to design lightweight and efficient SNNs, we propose
a new approach named LitE-SNN that incorporates both spatial and temporal
compression into the automated network design process. Spatially, we present a
novel Compressive Convolution block (CompConv) to expand the search space to
support pruning and mixed-precision quantization. Temporally, we are the first
to propose a compressive timestep search to identify the optimal number of
timesteps under specific computation cost constraints. Finally, we formulate a
joint optimization to simultaneously learn the architecture parameters and
spatial-temporal compression strategies to achieve high performance while
minimizing memory and computation costs. Experimental results on CIFAR-10,
CIFAR-100, and Google Speech Command datasets demonstrate our proposed
LitE-SNNs can achieve competitive or even higher accuracy with remarkably
smaller model sizes and fewer computation costs.
