Dedicated hardware for spiking neural networks (SNN) reduces energy consumption with
spike-driven computing. This paper proposes a graph-based spatio-temporal …

Spiking neural networks (SNNs) that enable low-power design on edge devices have
recently attracted significant research. However, the temporal characteristic of SNNs causes …

Larger Spiking Neural Network (SNN) models are typically favorable as they can offer higher
accuracy. However, employing such models on the resource-and energy-constrained …

A spiking neural network (SNN) with the energy-efficient and low-cost processor is
presented in this paper, which is based on mechanism with increased biological plausibility …

Spiking neural networks (SNNs) are inspired from biological brains and have demonstrated
great energy efficiency on hardware computing platforms. However, it is a challenge to …

Spiking neural networks (SNNs) are bio-inspired neural networks with asynchronous
discrete and sparse characteristics, which have increasingly manifested their superiority in …

Deep Neural Networks (DNNs) such as convolutional neural networks (CNNs) have become
state-of-the-art methods for diverse fields, such as computer vision, natural language …

Spiking Neural Networks (SNNs) are energy efficient alternatives to commonly used deep
neural networks (DNNs). Through event-driven information processing, SNNs can reduce …

Spiking Neural Networks (SNNs) have emerged as an attractive alternative to traditional
deep learning frameworks, since they provide higher computational efficiency in event …

Spiking Neural Networks (SNNs) can offer ultra low power/energy consumption for machine
learning-based applications due to their sparse spike-based operations. Currently, most of …