Energy consumption represents one of the most relevant issues by now in operating computing infrastructures, from traditional High Performance Computing Centers to Cloud Data Centers. Low power System-on-Chip (SoC) architectures, originally developed in the context of mobile and embedded technologies, are becoming attractive also for scientific and industrial applications given their increasing computing performances, coupled with relatively low costs and power demands. In this paper, we investigate the performance of the most representative SoCs for a computational intensive N-body benchmark, a simple deep learning based application and a real-life application taken from the field of molecular biology. The goal is to assess the trade-off among time-to-solution, energy-to-solution and economical aspects for both scientific and commercial purposes they are able to achieve in comparison to traditional server-grade architectures adopted in present infrastructures.
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