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

Purpose Big data is increasingly becoming a major organizational enterprise force to reckon
with in this global era for all sizes of industries. It is a trending new enterprise system or …

In this paper, the performance of three machine-learning methods for predicting short-term
evolution and for reproducing the long-term statistics of a multiscale spatiotemporal Lorenz …

Nowadays, there are two significant tendencies, how to process the enormous amount of
data, big data, and how to deal with the green issues related to sustainability and …

Approximate computing has gained research attention recently as a way to increase energy
efficiency and/or performance by exploiting some applications' intrinsic error resiliency …

Recent advances in computing algorithms and hardware have rekindled interest in
developing high-accuracy, low-cost surrogate models for simulating physical systems. The …

In this paper, approximate SRAMs are explored in the context of error-tolerant applications,
in which energy is saved at the cost of the occurrence of read/write errors (ie, signal quality …

Approximate computing deviates from long-held paradigm and attracted towards intrinsic
application resilience to enhance the efficiency by relaxing the parameter of full accuracy …

This paper proposes a method to promote power saving in the computational mechanics
simulations by deep learning. The method is expected to contribute to the achievement of …

With the rising variation and complexity of embedded work-loads, FPGA-based systems are
being increasingly used for many applications. The reconfigurability and high parallelism …