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

The rapid growth of demanding applications in domains applying multimedia processing
and machine learning has marked a new era for edge and cloud computing. These …

The boom in the capabilities and features of mobile devices, like smartphones, tablets, and
wearables, combined with the ubiquitous and affordable Internet access and the advances …

Advanced computing systems have long been enablers for breakthroughs in artificial
intelligence (AI) and machine learning (ML) algorithms, either through sheer computational …

The challenging deployment of compute-intensive applications from domains such as
Artificial Intelligence (AI) and Digital Signal Processing (DSP), forces the community of …

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 …

Energy-efficient computing is a much needed technological advantage for future.
Approximate or inexact computing is a computing paradigm that can trade energy and …

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 …

Approximate computing environments trade off computational accuracy for improvements in
performance, energy, and resiliency cost. For widespread adoption of approximate …

Approximate Computing (AxC) paradigm aims at designing computing systems that can
satisfy the rising performance demands and improve the energy efficiency. AxC exploits the …