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 …

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 …

Approximate computing is known for its effectiveness in improvising the energy efficiency of
deep neural network (DNN) accelerators at the cost of slight accuracy loss. Very recently, the …

Deep neural networks (DNNs) are being heavily utilized in modern applications, putting
energy-constraint devices to the test. To bypass high energy consumption issues …

Deep Neural Networks (DNNs) have been heavily utilized in modern applications, imposing
substantial challenges on embedded devices with limited resources. In the past few years …

This paper reports a runtime accuracy reconfigurable implementation of an energy efficient
deep learning accelerator. The accelerator utilizes the voltage overscaling (VOS) technique …

In recent years there has been an increase in the use of artificial intelligence and other data-
based techniques to automate decision-making in companies, and discover new knowledge …