Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT)/Internet of
Everything (IoE), and Cyber Physical Systems (CSP) pose incessantly escalating demands …

As convolutional neural networks (CNNs) enable state-of-the-art computer vision
applications, their high energy consumption has emerged as a key impediment to their …

Approximate computing is a very promising design paradigm for crossing the CPU power
wall, primarily driven by the potential to sacrifice output quality for significant gains in …

This paper presents an approach to enhance the performance of machine learning
applications based on hardware acceleration. This approach is based on parameterised …

The rapid pace and successful application of machine learning research and development
has seen widespread deployment of deep convolutional neural networks (CNNs). Alongside …

In recent years, inexact computing has been increasingly regarded as one of the most
promising approaches for reducing energy consumption in many applications that can …

In the near future, cameras will be used everywhere as flexible sensors for numerous
applications. For mobility and privacy reasons, the required image processing should be …

We present a novel dynamic configuration technique for deep neural networks that permits
step-wise energy-accuracy tradeoffs during runtime. Our configuration technique adjusts the …

Recent technology trends have indicated that, although device sizes will continue to scale
as they have in the past, supply voltage scaling has ended. As a result, future chips can no …

Applications with large amounts of data, real-time constraints, ultra-low power requirements,
and heavy computational complexity present significant challenges for modern computing …