Approximate computing is gaining traction as a computing paradigm for data analytics and
cognitive applications that aim to extract deep insight from vast quantities of data. In this …

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 …

Program autotuning has been shown to achieve better or more portable performance in a
number of domains. However, autotuners themselves are rarely portable between projects …

Energy is increasingly a first-order concern in computer systems. Exploiting energy-accuracy
trade-offs is an attractive choice in applications that can tolerate inaccuracies. Recent work …

Deep Convolutional Neural Networks (CNNs) perform billions of operations for classifying a
single input. To reduce these computations, this paper offers a solution that leverages a …

Energy has become a first-class design constraint in computer systems. Memory is a
significant contributor to total system power. This paper introduces Flikker, an application …

Energy-efficient computing is important in several systems ranging from embedded devices
to large scale data centers. Several application domains offer the opportunity to tradeoff …

Many modern computations (such as video and audio encoders, Monte Carlo simulations,
and machine learning algorithms) are designed to trade off accuracy in return for increased …

Approximate computing, where computation accuracy is traded off for better performance or
higher data throughput, is one solution that can help data processing keep pace with the …