Abstract Spiking Neural Networks (SNNs) provide an energy-efficient deep learning option
due to their unique spike-based event-driven (ie, spike-driven) paradigm. In this paper, we …

Pruning for Spiking Neural Networks (SNNs) has emerged as a fundamental methodology
for deploying deep SNNs on resource-constrained edge devices. Though the existing …

Deploying energy-efficient deep learning algorithms on computational-limited devices, such
as robots, is still a pressing issue for real-world applications. Spiking Neural Networks …

Spiking Neural Networks (SNNs), a novel brain-inspired algorithm, are garnering increased
attention for their superior computation and energy efficiency over traditional artificial neural …

TinyML is an emerging subfield of machine learning in which machine learning algorithms
can be deployed in resource-starving devices, in order for them to process their own data …