We review the coevolution of hardware and software dedicated to neuromorphic systems.
From modest beginnings, these disciplines have become central to the larger field of …

Spiking Neural Networks (SNNs), known as the third generation of the neural network, are
famous for their biological plausibility and brain-like characteristics. Recent efforts further …

This paper investigates the performance of deep convolutional spiking neural networks
(DCSNNs) trained using spike-based backpropagation techniques. Specifically, the study …

Spiking Neural Networks (SNNs) offer a promising alternative to Artificial Neural Networks
(ANNs) for deep learning applications, particularly in resource-constrained systems. This is …

The era of artificial intelligence of things (AIoT) brings huge challenges on edge visual
processing systems under strict processing latency, cost and energy budgets. The …

Spiking Neural Network (SNN) has the promising ability to take advantage of the spatio-
temporal information from Electroencephalograms (EEG) for emotion recognition. However …

Spiking neural networks (SNNs) have shown great potential in achieving high energy
efficiency and low power consumption compared to artificial neural networks (ANNs) …

Integrate-and-fire (IAF) and leaky integrate-and-fire (LIF) models are the popular models for
spiking neurons and spiking neuron networks (SNN). They lack the dynamic properties of …

Radar emitter classification (REC) plays an important role in modern warfare. Traditional
REC methods have difficulty identifying complex radar signals in the present day. Inspired …

Convolutional Neural Networks (CNNs) are widely employed to solve various problems, eg,
image classification. Due to their compute-and data-intensive nature, CNN accelerators …