Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

Low-latency deep spiking neural networks (SNNs) have become a promising alternative to
conventional artificial neural networks (ANNs) because of their potential for increased …

Spiking neural networks (SNNs) are promising to be widely deployed in real-time and safety-
critical applications with the advance of neuromorphic computing. Recent work has …

Spiking Neural Networks (SNNs), the third generation NNs, have come under the spotlight
for machine learning based applications due to their biological plausibility and reduced …

Machine learning, in particular deep learning, is being used in almost all the aspects of life
to facilitate humans, specifically in mobile and Internet of Things (IoT)-based applications …

Spiking neural networks (SNNs) have shown much promise as an energy-efficient
alternative to artificial neural networks (ANNs). Such methods trained by surrogate gradient …

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 …

Spiking neural network (SNN) is broadly deployed in neuromorphic devices to emulate brain
function. In this context, SNN security becomes important while lacking in-depth …

Deep Learning (DL) algorithms have gained popularity owing to their practical problem-
solving capacity. However, they suffer from a serious integrity threat, ie, their vulnerability to …

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