In computer vision, a series of exemplary advances have been made in several areas
involving image classification, semantic segmentation, object detection, and image super …

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 …

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 …

Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

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

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 …

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

Deep artificial neural networks apply principles of the brain's information processing that led
to breakthroughs in machine learning spanning many problem domains. Neuromorphic …

It is widely believed the brain-inspired spiking neural networks have the capability of
processing temporal information owing to their dynamic attributes. However, how to …

Abstract Spiking Neural Networks (SNNs) have emerged as a biology-inspired method
mimicking the spiking nature of brain neurons. This bio-mimicry derives SNNs' energy …