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 …

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models.
Recent progress in training methods has enabled successful deep SNNs on large-scale …

Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …

Spiking neural networks (SNNs) are brain-inspired models that enable energy-efficient
implementation on neuromorphic hardware. However, the supervised training of SNNs …

Abstract Spiking Neural Networks (SNNs) are promising energy-efficient models for
neuromorphic computing. For training the non-differentiable SNN models, the …

This paper proposes a low latency neural network architecture for event-based dense
prediction tasks. Conventional architectures encode entire scene contents at a fixed rate …

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) have shown advantages in computation and energy
efficiency over traditional artificial neural networks (ANNs) thanks to their event-driven …

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

Abstract Spiking Neural Network (SNN) is a promising energy-efficient neural architecture
when implemented on neuromorphic hardware. The Artificial Neural Network (ANN) to SNN …