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Publication Detail
Deep parameter optimisation on android smartphones for energy minimisation-a tale ofwoe and a proof-of-concept
  • Publication Type:
    Conference
  • Authors:
    Bokhari MA, Bruce BR, Alexander B, Wagner M
  • Publisher:
    ACM
  • Publication date:
    15/07/2017
  • Pagination:
    1501, 1508
  • Published proceedings:
    GECCO 2017 - Proceedings of the Genetic and Evolutionary Computation Conference Companion
  • ISBN-13:
    9781450349390
  • Status:
    Published
  • Name of conference:
    GECCO '17: Genetic and Evolutionary Computation Conference
  • Conference place:
    Berlin, Germany
  • Conference start date:
    15/07/2017
  • Conference finish date:
    19/07/2017
Abstract
With power demands of mobile devices rising, it is becoming increasingly important to make mobile soffware applications more energy effcient. Unfortunately, mobile platforms are diverse and very complex which makes energy behaviours diffcult to model. is complexity presents challenges to the e$ectiveness of off-line optimisation of mobile applications. In this paper, we demonstrate that it is possible to automatically optimise an application for energy on a mobile device by evaluating energy consumption in-vivo. In contrast to previous work, we use only the device's own internal meter. Our approach involves many technical challenges but represents a realistic path toward learning hardware specific energy models for program code features.
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