Spiking neural networks (SNNs) offer a promising energy-efficient alternative to artificial
neural networks (ANNs), in virtue of their high biological plausibility, rich spatial-temporal …

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks, which is suitable to be implemented on low-power …

Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental
theories, models, hardware architectures, and application systems toward more general …

Spiking Neural Networks (SNNs) have emerged as energy-efficient alternatives to Artificial
Neural Networks (ANNs) when deployed on neuromorphic chips. While recent studies have …

Spiking neural networks (SNNs) are an active research domain toward energy-efficient
machine intelligence. Compared to conventional artificial neural networks (ANNs), SNNs …

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 …

Spiking Neural Networks (SNNs) have recently attracted widespread research interest as an
efficient alternative to traditional Artificial Neural Networks (ANNs) because of their capability …

Spiking Neural Networks (SNNs) have gained increasing attention as energy-efficient neural
networks owing to their binary and asynchronous computation. However, their non-linear …

Spiking Neural Networks (SNNs) have gained significant research attention over the past
decade due to their potential for enabling resource-constrained edge devices. While existing …