Biologically inspired Spiking Neural Networks (SNNs) have attracted significant attention for their ability to provide extremely energy-efficient machine intelligence through event-driven operation and sparse activities. As artificial intelligence (AI) becomes ever more democratized, there is an increasing need to execute SNN models on edge devices. Existing works adopt weight pruning to reduce SNN model size and accelerate inference. However, these methods mainly focus on how to obtain a sparse model for efficient inference, rather than training efficiency. To overcome these drawbacks, in this paper, we propose a Neurogenesis Dynamics-inspired Spiking Neural Network training acceleration framework, NDSNN. Our framework is computational efficient and trains a model from scratch with dynamic sparsity without sacrificing model fidelity. Specifically, we design a new drop-and-grow strategy with decreasing number of non-zero weights, to maintain extreme high sparsity and high accuracy. We evaluate NDSNN using VGG-16 and ResNet-19 on CIFAR-10, CIFAR-100 and TinyImageNet. Experimental results show that NDSNN achieves up to 20.52\% improvement in accuracy on Tiny-ImageNet using ResNet-19 (with a sparsity of 99\%) as compared to other SOTA methods (e.g., Lottery Ticket Hypothesis (LTH), SET-SNN, RigL-SNN). In addition, the training cost of NDSNN is only 40.89\% of the LTH training cost on ResNet-19 and 31.35\% of the LTH training cost on VGG-16 on CIFAR-10.

翻译：生物启发的脉冲神经网络（SNNs）因其通过事件驱动操作和稀疏活动实现极高能效机器智能的能力而受到广泛关注。随着人工智能（AI）日益普及，在边缘设备上执行SNN模型的需求不断增长。现有研究采用权重剪枝来减小SNN模型规模并加速推理，但这些方法主要关注如何获得稀疏模型以实现高效推理，而非训练效率。为克服这些不足，本文提出一种神经发生动力学启发的脉冲神经网络训练加速框架NDSNN。该框架具有计算高效性，能从零开始训练具有动态稀疏性的模型，且不牺牲模型保真度。具体而言，我们设计了一种新的"丢弃-生长"策略，通过减少非零权重的数量来维持极高的稀疏性和高精度。我们在CIFAR-10、CIFAR-100和TinyImageNet上使用VGG-16和ResNet-19评估了NDSNN。实验结果表明，与其他现有最先进方法（如彩票假设（LTH）、SET-SNN、RigL-SNN）相比，NDSNN在TinyImageNet上使用ResNet-19（稀疏度为99%）时，准确率提升高达20.52%。此外，在CIFAR-10上，NDSNN的训练成本仅为ResNet-19上LTH训练成本的40.89%，以及VGG-16上LTH训练成本的31.35%。