  Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of
event-driven and high energy-efficient, which are different from traditional
Artificial Neural Networks (ANNs) when deployed on edge devices such as
neuromorphic chips. Most previous work focuses on SNNs training strategies to
improve model performance and brings larger and deeper network architectures.
It is difficult to deploy these complex networks on resource-limited edge
devices directly. To meet such demand, people compress SNNs very cautiously to
balance the performance and the computation efficiency. Existing compression
methods either iteratively pruned SNNs using weights norm magnitude or
formulated the problem as a sparse learning optimization. We propose an
improved end-to-end Minimax optimization method for this sparse learning
problem to better balance the model performance and the computation efficiency.
We also demonstrate that jointly applying compression and finetuning on SNNs is
better than sequentially, especially for extreme compression ratios. The
compressed SNN models achieved state-of-the-art (SOTA) performance on various
benchmark datasets and architectures. Our code is available at
https://github.com/chenjallen/Resource-Constrained-Compression-on-SNN.
