The past decade has witnessed the great success of deep neural networks in various
domains. However, deep neural networks are very resource-intensive in terms of energy …

The potential of neuromorphic computing to bring about revolutionary advancements in
multiple disciplines, such as artificial intelligence (AI), robotics, neurology, and cognitive …

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) are gaining interest due to their event-driven processing
which potentially consumes low-power/energy computations in hardware platforms while …

Human brain cortex acts as a rich inspiration source for constructing efficient artificial
cognitive systems. In this paper, we investigate to incorporate multiple brain-inspired …

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 …

Neuromorphic computing is emerging to be a disruptive computational paradigm that
attempts to emulate various facets of the underlying structure and functionalities of the brain …

To maximize the performance and energy efficiency of Spiking Neural Network (SNN)
processing on resource-constrained embedded systems, specialized hardware …

Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mobile robots
have shown huge potential for improving human productivity. These mobile agents require …

Larger Spiking Neural Network (SNN) models are typically favorable as they can offer higher
accuracy. However, employing such models on the resource-and energy-constrained …