LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization

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 LitESNN 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 while utilizing the shared weights and pruning mask to reduce the computation. 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 CIFAR10, CIFAR100, and Google Speech Command datasets demonstrate our proposed LitESNNs can achieve competitive or even higher accuracy with remarkably smaller model sizes and fewer computation costs. Furthermore, we validate the effectiveness of our LitESNN on the trade-off between accuracy and resource cost and show the superiority of our joint optimization. Additionally, we conduct energy analysis to further confirm the energy efficiency of LitESNN