Deep convolutional neural networks (CNNs) have shown great potential for numerous real-
world machine learning applications, but performing inference in large CNNs in real-time …

Spiking neural networks (SNNs) are considered as a potential candidate to overcome
current challenges, such as the high-power consumption encountered by artificial neural …

Abstract Spiking Neural Networks (SNNs) can be energy efficient alternatives to commonly
used deep neural networks (DNNs). However, computation over multiple timesteps …

Spiking neural networks (SNNs) have emerged as a promising brain inspired neuromorphic-
computing paradigm for cognitive system design due to their inherent event-driven …

Norse is a library that aims to exploit the advantages of bio-inspired neural components,
which are sparse and event-driven-a fundamental difference from artificial neural networks …

Spiking Neural Networks (SNNs), as bio-inspired energy-efficient neural networks, have
attracted great attentions from researchers and industry. The most efficient way to train deep …

Brain-inspired spiking neural networks (SNNs) have recently drawn more and more
attention due to their event-driven and energy-efficient characteristics. The integration of …

Abstract Spiking Neural Networks (SNNs) are well known as a promising energy-efficient
alternative to conventional artificial neural networks. Subject to the preconceived impression …

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 …

For a biological agent operating under environmental pressure, energy consumption and
reaction times are of critical importance. Similarly, engineered systems are optimized for …