The remarkable performance of deep Convolutional neural networks (CNNs) is generally
attributed to their deeper and wider architectures, which can come with significant …

The spiking neural network (SNN), as a promising brain-inspired computational model with
binary spike information transmission mechanism, rich spatially-temporal dynamics, and …

Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic
chips with high energy efficiency by introducing neural dynamics and spike properties. As …

Abstract Spiking Neural Network (SNN), recognized as a type of biologically plausible
architecture, has recently drawn much research attention. It transmits information by $0/1 …

Spike-based machine intelligence has recently attracted increasing research attention, and
has been considered as a promising approach towards artificial general intelligence (AGI). It …

Spiking neural networks (SNNs) have shown advantages in computation and energy
efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven …

Spiking neural networks (SNNs) have gained huge attention as a potential energy-efficient
alternative to conventional artificial neural networks (ANNs) due to their inherent high …

Abstract Most existing Spiking Neural Network (SNN) works state that SNNs may utilize
temporal information dynamics of spikes. However, an explicit analysis of temporal …

Pruning for Spiking Neural Networks (SNNs) has emerged as a fundamental methodology
for deploying deep SNNs on resource-constrained edge devices. Though the existing …

Spiking Neural networks (SNN) have emerged as an attractive spatio-temporal computing
paradigm for a wide range of low-power vision tasks. However, state-of-the-art (SOTA) SNN …