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 …

Reliability concerns due to technology scaling have been a major focus of researchers and
designers for several technology nodes. Therefore, many new techniques for enhancing and …

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 …

Machine learning is commonly being used in almost all the areas that involve advanced
data analytics and intelligent control. From applications like Natural Language Processing …

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 …

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

Specialized hardware accelerators have been designed and employed to maximize the
performance efficiency of Spiking Neural Networks (SNNs). However, such accelerators are …

Functional safety is a key requirement in several application domains in which
microprocessors are an essential part. A number of redundancy techniques have been …

A compilation technique for reliability-aware software transformations is presented. An
instruction-level reliability estimation technique quantifies the effects of hardware-level faults …

Over the years, significant work has been done on high-integrity systems, such as those
found in cars, satellites and aircrafts, to minimize the risk that a logic fault causes a system …