Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural networks owing to their binary and asynchronous computation. However, their non-linear activation, that is Leaky-Integrate-and-Fire (LIF) neuron, requires additional memory to store a membrane voltage to capture the temporal dynamics of spikes. Although the required memory cost for LIF neurons significantly increases as the input dimension goes larger, a technique to reduce memory for LIF neurons has not been explored so far. To address this, we propose a simple and effective solution, EfficientLIF-Net, which shares the LIF neurons across different layers and channels. Our EfficientLIF-Net achieves comparable accuracy with the standard SNNs while bringing up to ~4.3X forward memory efficiency and ~21.9X backward memory efficiency for LIF neurons. We conduct experiments on various datasets including CIFAR10, CIFAR100, TinyImageNet, ImageNet-100, and N-Caltech101. Furthermore, we show that our approach also offers advantages on Human Activity Recognition (HAR) datasets, which heavily rely on temporal information.

翻译：脉冲神经网络（SNNs）因其二进制异步计算特性，作为节能神经网络备受关注。然而，其非线性激活函数——漏-整合-发放（LIF）神经元需要额外内存存储膜电压以捕捉脉冲的时间动态特性。尽管LIF神经元所需内存成本随输入维度增大而显著增加，但目前尚未有研究探索降低LIF神经元内存占用的技术。为此，我们提出了一种简单而有效的解决方案EfficientLIF-Net，在不同层和通道间共享LIF神经元。我们的EfficientLIF-Net在保持与标准SNNs相当精度的同时，分别在LIF神经元的前向和反向传播中实现了约4.3倍和21.9倍的内存效率提升。我们在CIFAR10、CIFAR100、TinyImageNet、ImageNet-100及N-Caltech101等多个数据集上进行了实验。此外，我们还证明该方法在高度依赖时间信息的人类活动识别（HAR）数据集上同样具有优势。