Bringing Energy Efficiency Closer to Application Developers: An Extensible Software Analysis Framework

Abstract

Green, sustainable and energy-aware computing terms are gaining more and more attention during the last years. The increasing complexity of Internet of Things (IoT) applications makes energy efficiency an important requirement, imposing new challenges to software developers. Software tools capable of providing energy consumption estimations and identifying optimization opportunities are critical during all the phases of application development. This work proposes a novel framework that targets the energy efficiency at application development level. The proposed framework is implemented as a single user-friendly tool-flow, providing a variety of useful features, such as the estimation of the energy consumption without the need of executing the application on the targeted IoT devices and the estimation of potential gains by GPU acceleration on modern heterogeneous IoT architectures. The proposed methodology provides several novel contributions, such as the combination of static analysis and dynamic instrumentation approaches in order to exploit the advantages of both. The framework is evaluated on widely used benchmarks, achieving increased estimation accuracy (more than 90% for similar architectures and more than 72% for the potential use of the GPU). The effectiveness of the framework is further demonstrated using two industrial use-cases achieving an energy reduction from 91% up to 98%.