After Deep Learning (DL) regained popularity recently, the Artificial Intelligence (AI) or
Machine Learning (ML) field is undergoing rapid growth concerning research and real-world …

Machine Learning (ML) has emerged as an attractive and viable technique to provide
effective solutions for a wide range of application domains. An important application domain …

Internet-of-Things (IoT) technology is increasingly prominent in the current stage of social
development. All walks of life have begun to implement the IoT integration technology, so as …

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

Deep neural networks (DNNs) are showing superior advantages in different domains and
are opening their path into critical applications where reliability is the main concern. DNNs …

Negative Biased Temperature Instability (NBTI)-induced aging is one of the critical reliability
threats in nano-scale devices. This paper makes the first attempt to study the NBTI aging in …

Convolutional Neural Networks (CNN) have shown impressive performance in computer
vision, natural language processing, and many other applications, but they exhibit high …

From tiny pacemaker chips to aircraft collision avoidance systems, the state-of-the-art Cyber-
Physical Systems (CPS) have increasingly started to rely on Deep Neural Networks (DNNs) …

The paper addresses some of the opportunities and challenges related to test and reliability
of three major emerging computing paradigms; ie, Quantum Computing, Computing engines …