Approximate computing trades off computation quality with effort expended, and as rising
performance demands confront plateauing resource budgets, approximate computing has …

When a computational task tolerates a relaxation of its specification or when an algorithm
tolerates the effects of noise in its execution, hardware, system software, and programming …

As one of the most promising energy-efficient computing paradigms, approximate computing
has gained a lot of research attention in the past few years. This paper presents a survey of …

We propose a novel approach to reduce the computational cost of evaluation of
convolutional neural networks, a factor that has hindered their deployment in low-power …

Mobile and IoT scenarios increasingly involve interactive and computation intensive
contextual recognition. Existing optimizations typically resort to computation offloading or …

Approximate computing can be employed for an emerging class of applications from various
domains such as multimedia, machine learning and computer vision. The approximated …

Batteryless energy-harvesting devices eliminate the need in batteries for deployed sensor
systems, enabling longer lifetime and easier maintenance. However, such devices cannot …

Approximate computing trades off accuracy for better performance and energy efficiency. It
offers promising optimization opportunities for a wide variety of modern applications, from …

Graphics Processing Units (GPUs) can accelerate diverse classes of applications, such as
recognition, gaming, data analytics, weather prediction, and multimedia. Many of these …

The trend of unsustainable power consumption and large memory bandwidth demands in
massively parallel multicore systems, with the advent of the big data era, has brought upon …