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

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 …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

Spiking Neural Networks (SNNs) have recently emerged as a prominent neural computing
paradigm. However, the typical shallow SNN architectures have limited capacity for …

In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …

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 …

Spiking neural networks (SNNs) are biologically plausible and computationally powerful.
The current computing systems based on the von Neumann architecture are almost the …

Historically, memory technologies have been evaluated based on their storage density, cost,
and latencies. Beyond these metrics, the need to enable smarter and intelligent computing …

As data movement operations and power-budget become key bottlenecks in the design of
computing systems, the interest in unconventional approaches such as processing-in …

Spiking neural networks (SNNs) have recently emerged as the low-power alternative to
artificial neural networks (ANNs) because of their sparse, asynchronous, and binary event …