  As the third-generation neural network, the Spiking Neural Network (SNN) has
the advantages of low power consumption and high energy efficiency, making it
suitable for implementation on edge devices. More recently, the most advanced
SNN, Spikformer, combines the self-attention module from Transformer with SNN
to achieve remarkable performance. However, it adopts larger channel dimensions
in MLP layers, leading to an increased number of redundant model parameters. To
effectively decrease the computational complexity and weight parameters of the
model, we explore the Lottery Ticket Hypothesis (LTH) and discover a very
sparse ($\ge$90%) subnetwork that achieves comparable performance to the
original network. Furthermore, we also design a lightweight token selector
module, which can remove unimportant background information from images based
on the average spike firing rate of neurons, selecting only essential
foreground image tokens to participate in attention calculation. Based on that,
we present SparseSpikformer, a co-design framework aimed at achieving sparsity
in Spikformer through token and weight pruning techniques. Experimental results
demonstrate that our framework can significantly reduce 90% model parameters
and cut down Giga Floating-Point Operations (GFLOPs) by 20% while maintaining
the accuracy of the original model.
