Deep Neural Networks (DNNs) are nowadays a common practice in most of the Artificial
Intelligence (AI) applications. Their ability to go beyond human precision has made these …

As DNNs become increasingly common in mission-critical applications, ensuring their
reliable operation has become crucial. Conventional resilience techniques fail to account for …

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 …

Currently, machine learning (ML) techniques are at the heart of smart cyber-physical
systems (CPSs) and Internet-of-Things (loT). This article discusses various challenges and …

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 …

Convolutional neural networks (CNNs) are becoming more and more important for solving
challenging and critical problems in many fields. CNN inference applications have been …

Deep Neural Networks (DNNs) are widely being adopted for safety-critical applications, eg,
healthcare and autonomous driving. Inherently, they are considered to be highly error …

Deep learning tasks cover a broad range of domains and an even more extensive range of
applications, from entertainment to extremely safety-critical fields. Thus, Deep Neural …

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

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 …