Neuromorphic computing is henceforth a major research field for both academic and
industrial actors. As opposed to Von Neumann machines, brain-inspired processors aim at …

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 …

Spiking neural networks (SNNs) that enables energy efficient implementation on emerging
neuromorphic hardware are gaining more attention. Yet now, SNNs have not shown …

Abstract Machine learning is yielding unprecedented interest in research and industry, due
to recent success in many applied contexts such as image classification and object …

Despite the success of Deep Neural Networks—a type of Artificial Neural Network (ANN)—in
problem domains such as image recognition and speech processing, the energy and …

Artificial neural networks (ANNs) have experienced a rapid advancement for their success in
various application domains, including autonomous driving and drone vision. Researchers …

Over the past few years, Spiking Neural Networks (SNNs) have become popular as a
possible pathway to enable low-power event-driven neuromorphic hardware. However, their …

While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …

Guided by brain-like 'spiking'computational frameworks, neuromorphic computing—brain-
inspired computing for machine intelligence—promises to realize artificial intelligence while …

Spiking Neural Networks (SNNs) operate with asynchronous discrete events (or spikes)
which can potentially lead to higher energy-efficiency in neuromorphic hardware …