Abstract Spiking Neural Networks (SNNs) have attracted significant attention due to their
energy-efficient properties and potential application on neuromorphic hardware. State-of-the …

Spiking neural networks (SNNs) exploit neural spikes to provide solutions for low-power
intelligent applications on neuromorphic hardware. Although SNNs have high computational …

Spiking Neural Networks (SNNs) have received widespread attention in academic
communities due to their superior spatio-temporal processing capabilities and energy …

Spiking neural networks (SNNs) provide an energy-efficient alternative to a variety of
artificial neural network (ANN) based AI applications. As the progress in neuromorphic …

Spiking neural networks (SNNs) offer promise for efficient and powerful neurally inspired
computation. Common to other types of neural networks, however, SNNs face the severe …

Spiking Neural Networks (SNNs) are considered the next generation of deep neural
networks for their computation efficiency and biological plausibility. Still, SNN models can be …

Spiking neural networks (SNNs) are gaining popularity in deep learning due to their low
energy budget on neuromorphic hardware. However, they still face challenges in lacking …

Abstract Spiking Neural Networks (SNNs) are increasingly deployed in applications on
resource constraint embedding systems due to their low power. Unfortunately, SNNs are …

Neuromorphic intelligence is driven by spiking neural networks (SNNs) to achieve high
algorithmic performance. However, similar to artificial neural networks (ANNs), SNNs are …

The spiking neural networks are inspired by the biological neurons that employ binary
spikes to propagate information in the neural network. It has garnered considerable attention …