Cruz-Ayoroa, Arnaldo J.

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    Program vectorization for reducing energy consumption in embedded systems
    (2014) Cruz-Ayoroa, Arnaldo J.; Jiménez-Cedeño, Manuel; College of Engineering; Arce-Nazario, Rafael; Santiago, Nayda; Department of Electrical and Computer Engineering; Vasquez-Urbano, Pedro
    When it comes to software optimization, speedup is the rst goal that comes to mind. However, as the integration level of electronic circuits continues its exponential growth, power reduction has also become an important goal. In recent years, the proliferation of mobile devices has also been a driver for reducing energy consumption in embedded systems. Although hardware engineers are already well acquainted with design techniques for low power consumption, software power reduction is still a vastly unexplored topic particularly in the area of compilation; even though, ultimately, software is the main responsible of making ecient use of the hardware. This work proposes a machine learning driven optimization ow that uses a highlevel characterization approach on a program's source code and training with optimization protability measurements to predict whether to apply a particular optimization if energy consumption is expected to be reduced. The optimization studied in this work is called vectorization, which was found to be not only powerful for reducing execution time, but when applied correctly to also reduce power and energy consumption. Experiments were conducted on an implementation of the popular ARM Cortex-A8. Nevertheless, this methodology is not limited to this particular embedded architecture. A predictor was trained which ii decided whether vectorization would have a benecial or detrimental impact with 74% precision, resulting in an average of 64% decrease in energy consumption and only 5% increase in energy in the case of mis-predictions.