The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

With the adoption of smart systems, artificial neural networks (ANNs) have become
ubiquitous. Conventional ANN implementations have high energy consumption, limiting …

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 Networks (SNNs) provide an energy-efficient deep learning option
due to their unique spike-based event-driven (ie, spike-driven) paradigm. In this paper, we …

The brain is the perfect place to look for inspiration to develop more efficient neural
networks. The inner workings of our synapses and neurons provide a glimpse at what the …

We consider two biologically plausible structures, the Spiking Neural Network (SNN) and the
self-attention mechanism. The former offers an energy-efficient and event-driven paradigm …

Abstract Spiking Neural Network (SNN) is a promising energy-efficient AI model when
implemented on neuromorphic hardware. However, it is a challenge to efficiently train SNNs …

Recently, brain-inspired spiking neuron networks (SNNs) have attracted widespread
research interest because of their event-driven and energy-efficient characteristics. Still, it is …

Spiking neural networks (SNNs) are brain-inspired energy-efficient models that encode
information in spatiotemporal dynamics. Recently, deep SNNs trained directly have shown …

Brain-inspired spiking neural networks (SNNs) are becoming a promising energy-efficient
alternative to traditional artificial neural networks (ANNs). However, the performance gap …