Deep Neural Networks (DNNs) are very popular because of their high performance in
various cognitive tasks in Machine Learning (ML). Recent advancements in DNNs have …

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 …

Current research in the area of Neural Networks (NN) has resulted in performance
advancements for a variety of complex problems. Especially, embedded system applications …

In the Machine Learning era, Deep Neural Networks (DNNs) have taken the spotlight, due to
their unmatchable performance in several applications, such as image processing, computer …

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 …

Libraries of approximate circuits are composed of fully characterized digital circuits that can
be used as building blocks of energy-efficient implementations of hardware accelerators …

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart
Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data …

In this work, we introduce a control variate approximation technique for low error
approximate Deep Neural Network (DNN) accelerators. The control variate technique is …

Recent breakthroughs in Neural Networks (NNs) have made DNN accelerators ubiquitous
and led to an ever-increasing quest on adopting them from Cloud to edge computing …

This paper investigates a runtime accuracy reconfigurable implementation of an energy
efficient deep learning accelerator. It is based on voltage overscaling (VOS) technique which …