Due to the high activation sparsity and use of accumulates (AC) instead of expensive
multiply-and-accumulates (MAC), neuromorphic spiking neural networks (SNNs) have …

The sustainable development goal 4 (SDG4) aims to “ensure inclusive and equitable quality
education and promote lifelong learning opportunities for all”, Therefore., researchers are …

Edge devices equipped with computer vision must deal with vast amounts of sensory data
with limited computing resources. Hence, researchers have been exploring different energy …

In the recent years, Spiking Neural Networks have gain much attention from the research
community. They can now be trained using the powerful gradient descent and have drifted …

Abstract Spiking Neural Networks (SNNs) that operate in an event-driven manner and
employ binary spike representation have recently emerged as promising candidates for …

Spiking neural networks (SNNs) are considered as a candidate for efficient deep learning
systems: these networks communicate with 0 or 1 spikes and their computations do not …

This paper introduces a 71.2-W speech recognition accelerator designed for edge devices'
real-time applications, emphasizing an ultra low power design. Achieved through algorithm …

Spiking Neural networks (SNN) have emerged as an attractive spatio-temporal computing
paradigm for a wide range of low-power vision tasks. However, state-of-the-art (SOTA) SNN …

Spiking Neural Networks (SNNs) as a biology-inspired method mimicking the signal
transmission of brain neurons have received great attention as a low-power alternative to …

Spiking Neural Networks (SNNs) as a biology-inspired method mimicking the signal
transmission of brain neurons can be managed in an event-driven manner, and generate …