As a bio-inspired and emerging sensor, an event-based neuromorphic vision sensor has a
different working principle compared to the standard frame-based cameras, which leads to …

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 …

Automotive embedded algorithms have very high constraints in terms of latency, accuracy
and power consumption. In this work, we propose to train spiking neural networks (SNNs) …

Spiking neural networks (SNNs) are a promising energy-efficient alternative to artificial
neural networks (ANNs) due to their rich dynamics, capability to process spatiotemporal …

Event cameras have recently been shown beneficial for practical vision tasks such as action
recognition thanks to their high temporal resolution power efficiency and reduced privacy …

Abstract Spiking Neural Networks (SNNs) have recently emerged as a new generation of
low-power deep neural networks due to sparse, asynchronous, and binary event-driven …

Neuromorphic data, recording frameless spike events, have attracted considerable attention
for the spatiotemporal information components and the event-driven processing fashion …

Recent years have witnessed remarkable achievements in video-based action recognition.
Apart from traditional frame-based cameras, event cameras are bio-inspired vision sensors …

Event-based visual, a new visual paradigm with bio-inspired dynamic perception and μ s
level temporal resolution, has prominent advantages in many specific visual scenarios and …

Event-based cameras have attracted increasing attention due to their advantages of
biologically inspired paradigm and low power consumption. Since event-based cameras …