  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.
