With the advent of low-power neuromorphic computing systems, new possibilities have
emerged for deployment in various sectors, like healthcare and transport, that require …

Autonomous Driving (AD) related features provide new forms of mobility that are also
beneficial for other kind of intelligent and autonomous systems like robots, smart …

The security and privacy concerns along with the amount of data that is required to be
processed on regular basis has pushed processing to the edge of the computing systems …

A prominent technique for reducing the memory footprint of Spiking Neural Networks (SNNs)
without decreasing the accuracy significantly is quantization. However, the state-of-the-art …

Spiking neural networks (SNNs) have shown a potential for having low energy with
unsupervised learning capabilities due to their biologically-inspired computation. However …

Spiking Neural Networks (SNNs), despite being energy-efficient when implemented on
neuromorphic hardware and coupled with event-based Dynamic Vision Sensors (DVS), are …

Autonomous mobile agents (eg, mobile ground robots and UAVs) typically require low-
power/energy-efficient machine learning (ML) algorithms to complete their ML-based tasks …

Recent advances have shown that Spiking Neural Network (SNN)-based systems can
efficiently perform unsuper-vised continual learning due to their bio-plausible learning rule …

Spiking Neural Networks (SNNs) have shown capabilities of achieving high accuracy under
unsupervised settings and low operational power/energy due to their bio-plausible …

Spiking Neural Networks (SNNs) offer a promising solution to achieve ultra low-
power/energy computation for solving machine learning tasks. Currently, most of the SNN …