Spiking Neural Networks (SNNs) have been an attractive option for deployment
on devices with limited computing resources and lower power consumption because
of the event-driven computing characteristic. As such devices have limited
computing and storage resources, pruning for SNNs has been widely focused
recently. However, the binary and non-differentiable property of spike signals
make pruning deep SNNs challenging, so existing methods require high time
overhead to make pruning decisions. In this paper, inspired by critical brain
hypothesis in neuroscience, we design a regeneration mechanism based on
criticality to efficiently obtain the critical pruned networks. Firstly, we
propose a low-cost metric for the criticality of pruning structures. Then we
re-rank the pruned structures after pruning and regenerate those with higher
criticality. We evaluate our method using VGG-16 and ResNet-19 for both
unstructured pruning and structured pruning. Our method achieves higher
performance compared to current state-of-the-art (SOTA) method with the same
time overhead. We also achieve comparable performances (even better on VGG-16)
compared to the SOTA method with 11.3x and 15.5x acceleration. Moreover, we
investigate underlying mechanism of our method and find that it efficiently
selects potential structures, learns the consistent feature representations and
reduces the overfitting during the recovery phase.

本文提出了一种采用重建机制来高效获取关键修剪网络的方法，通过基于关键修剪结构的低成本度量，重新排名修剪结构并重建那些具有更高关键性的结构，实现了比现有方法更高的性能和加速度，同时研究了机制潜在机理并发现了其在恢复阶段能够高效选择潜在结构、学习一致的特征表示和减少过拟合。

脑启发式高效剪枝：利用脉冲神经网络中的关键性质

Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking  Neural Networks

Event-based cameras display great potential for a variety of tasks such as
high-speed motion detection and navigation in low-light environments where
conventional frame-based cameras suffer critically. This is attributed to their
high temporal resolution, high dynamic range, and low-power consumption.
However, conventional computer vision methods as well as deep Analog Neural
Networks (ANNs) are not suited to work well with the asynchronous and discrete
nature of event camera outputs. Spiking Neural Networks (SNNs) serve as ideal
paradigms to handle event camera outputs, but deep SNNs suffer in terms of
performance due to the spike vanishing phenomenon. To overcome these issues, we
present Spike-FlowNet, a deep hybrid neural network architecture integrating
SNNs and ANNs for efficiently estimating optical flow from sparse event camera
outputs without sacrificing the performance. The network is end-to-end trained
with self-supervised learning on Multi-Vehicle Stereo Event Camera (MVSEC)
dataset. Spike-FlowNet outperforms its corresponding ANN-based method in terms
of the optical flow prediction capability while providing significant
computational efficiency.

该论文介绍了一个基于 Spike-FlowNet 的深度混合神经网络体系结构，用于有效地估计来自稀疏事件相机输出的光流，同时提供了显着的计算效率。