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

Spiking neural networks (SNNs) are an active research domain toward energy-efficient
machine intelligence. Compared to conventional artificial neural networks (ANNs), SNNs …

Spiking Neural Networks (SNNs) have recently emerged as the low-power alternative to
Artificial Neural Networks (ANNs) owing to their asynchronous, sparse, and binary …

Most existing visual object tracking approaches are implemented based on von Neumann
computation systems, which inevitably have the problems of high latency. Additionally …

This review explores the intersection of bio-plausible artificial intelligence in the form of
spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain …

Spiking Neural Networks (SNNs) have gained attention for their energy-efficient machine
learning capabilities, utilizing bio-inspired activation functions and sparse binary spike-data …

Resistive memory (ReRAM) or memristor devices offer the prospect of more efficient
computing. While memristors have been used for a variety of computing systems, their …

Recently several structured pruning techniques have been introduced for energy-efficient
implementation of Deep Neural Networks (DNNs) with lesser number of crossbars …

Deep neural network (DNN) accelerators employing crossbar arrays capable of in-memory
computing (IMC) are highly promising for neural computing platforms. However, in deeply …

Crossbar memory arrays have been touted as the workhorse of in-memory computing (IMC)-
based acceleration of Deep Neural Networks (DNNs), but the associated hardware non …