Energy consumption characterization of a Massively Parallel Processor Array (MPPA) platform running a hyperspectral SVM classifier

In this paper, a Massively Parallel Processor Array platform is characterized in terms of energy consumption using a Support Vector Machine for hyperspectral image classification. This platform gathers 16 clusters composed of 16 cores each, i.e., 256 processors working in parallel. The objective of the work is to associate power dissipation and energy consumed by the platform with the different resources of the architecture. Experimenting with a hyperspectral SVM classifier, this study has been conducted using three strategies: i) modifying the number of processing elements, i.e., clusters and cores, ii) increasing system frequency, and iii) varying the number of active communication links during the analysis, i.e., I/Os and DMAs. As a result, a relationship between the energy consumption and the active platform resources has been exposed using two different parallelization strategies. Finally, the implementation that fully exploits the parallelization possibilities working at 500MHz has been proven to be also the most efficient one, as it reduces the energy consumption by 98% when compared to the sequential version running at 400MHz.