Neuromorphic Computing, a concept pioneered in the late 1980s, is receiving a lot of
attention lately due to its promise of reducing the computational energy, latency, as well as …

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead
of capturing images at a fixed rate, they asynchronously measure per-pixel brightness …

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 …

Multiple neuromorphic systems use spiking neural networks (SNNs) to perform computation
in a way that is inspired by concepts learned about the human brain. SNNs are artificial …

Back side illumination has become standard image sensor technology owing to its superior
quantum efficiency and fill factor. A direct comparison of front and back side illumination (FSI …

In this work, we present an optical space imaging dataset using a range of event-based
neuromorphic vision sensors. The unique method of operation of event-based sensors …

It has been more than two decades since the first neuromorphic Dynamic Vision Sensor
(DVS) sensor was invented, and many subsequent prototypes have been built with a wide …

State-of-the-art machine-learning methods for event cameras treat events as dense
representations and process them with conventional deep neural networks. Thus, they fail to …

The nervous systems converts the physical quantities sensed by its primary receptors into
trains of events that are then processed in the brain. The unmatched efficiency in information …

The emergence of artificial intelligence has represented great potential in solving a wide
range of complex problems. However, traditional general-purpose chips based on von …