Spiking Neural Networks (SNNs) have been attached great importance due to
their biological plausibility and high energy-efficiency on neuromorphic chips.
As these chips are usually resource-constrained, the compression of SNNs is
thus crucial along the road of practical use of SNNs. Most existing methods
directly apply pruning approaches in artificial neural networks (ANNs) to SNNs,
which ignore the difference between ANNs and SNNs, thus limiting the
performance of the pruned SNNs. Besides, these methods are only suitable for
shallow SNNs. In this paper, inspired by synaptogenesis and synapse elimination
in the neural system, we propose gradient rewiring (Grad R), a joint learning
algorithm of connectivity and weight for SNNs, that enables us to seamlessly
optimize network structure without retraining. Our key innovation is to
redefine the gradient to a new synaptic parameter, allowing better exploration
of network structures by taking full advantage of the competition between
pruning and regrowth of connections. The experimental results show that the
proposed method achieves minimal loss of SNNs' performance on MNIST and
CIFAR-10 dataset so far. Moreover, it reaches a $\sim$3.5% accuracy loss under
unprecedented 0.73% connectivity, which reveals remarkable structure refining
capability in SNNs. Our work suggests that there exists extremely high
redundancy in deep SNNs. Our codes are available at
this https URL

本文提出了一种基于神经系统突触发生和消失机制的梯度重连算法，用于 SNNs 中的连接性和权重优化，实现了对网络结构的优化而无需重新训练，实验结果表明，该方法能够在 MNIST 和 CIFAR-10 数据集上实现对 SNNs 性能的最小损失，并且在 0.73% 的连接性下，只有约 3.5% 的精度损失，具有优秀的结构优化能力。