Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks

Oussama Djedidi (1, 2) , Mohand Djeziri (2)

1 AMU - Aix Marseille Université
2 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)

Power consumption in electronic systems is an essential feature for the management of energy autonomy, performance analysis, and the aging monitoring of components. Thus, several research studies have been devoted to the development of power models and profilers for embedded systems. Each of these models is designed to fit a specific usage context. This paper is a part of a series of works dedicated to modeling and monitoring embedded systems in airborne equipment. The objective of this paper is twofold. Firstly, it presents an overview of the most used models in the literature. Then, it offers a comparative analysis of these models according to a set of criteria, such as the modeling assumptions, the necessary instrumentation necessary, the accuracy, and the complexity of implementation. Secondly, we introduce a new power estimator for ARM-Based embedded systems, with component-level granularity. The estimator is based on NARX neural networks and used to monitor power for diagnosis purposes. The obtained experimental results highlight the advantages and limitations of the models presented in the literature and demonstrate the effectiveness of the proposed NARX, having obtained the best results in its class for a smartphone (An online Mean Absolute Percentage Error = 2.2%).

Mots clés: Power Consumption; neural Networks; Power consumption; Data fitting; Machine learning; Embedded systems; Modeling; Neural networks; Embedded Systems; Power profiling; NARX; Smartphone

Domaines: Machine Learning [stat.ML]; Energie électrique; Modélisation et simulation; Systèmes embarqués; Automatique

Oussama Djedidi, Mohand Djeziri. Power profiling and monitoring in embedded systems: A comparative study and a novel methodology based on NARX neural networks. Journal of Systems Architecture, 2020, 111, pp.101805. ⟨10.1016/j.sysarc.2020.101805⟩. ⟨hal-02740661⟩