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 …

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 …

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 …

We propose and evaluate a framework for creating and running approximation-enabled
MapReduce programs. Specifically, we propose approximation mechanisms that fit naturally …

Emerging high-performance architectures are anticipated to contain unreliable components
that may exhibit soft errors, which silently corrupt the results of computations. Full detection …

Approximate computing is an approach where reduced accuracy of results is traded off for
increased speed, throughput, or both. Loss of accuracy is not permissible in all computing …

The accuracy of an approximate computation is the distance between the result that the
computation produces and the corresponding fully accurate result. The reliability of the …

Variation in performance and power across manufactured parts and their operating
conditions is an accepted reality in modern microelectronic manufacturing processes with …

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 …

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