Constructing an Accurate and a High-Performance Power Profiler for Embedded Systems and Smartphones

Oussama Djedidi (1, 2) , Mohand Djeziri (2, 1) , Nacer M'Sirdi (2, 1) , Aziz Naamane (1, 2)

1 SASV - Systèmes Automatisés à Structure Variable
2 LIS - Laboratoire d'Informatique et des Systèmes (LIS) (Marseille, Toulon)

The main objective of this paper is to present a new accurate power profiler for embedded systems and smartphones. The second objective is, for it, to be a tutorial explaining the main steps to build power profilers for embedded and mobile systems, in general. We start our work by firstly describing the general methodology of building a power profiler. Then, we showcase how each step is undertaken to build a profiler with two power models. The first one was an artificial neural network (called N 2) that presented a lot of noise in its estimation. After debugging and improvement, the second model, a NARX neural network (we call N 3) was built. It eliminated all the drawback of the first model and had a mean absolute percentage error of 2.8%.

Mots clés: Data fitting; Embedded Systems; Modeling; NARX neural Networks; Power Consumption; Smartphone

Domaines: Informatique [cs]; Intelligence artificielle [cs.AI]; Automatique; Apprentissage [cs.LG]; Modélisation et simulation; Informatique mobile

Oussama Djedidi, Mohand Djeziri, Nacer M'Sirdi, Aziz Naamane. Constructing an Accurate and a High-Performance Power Profiler for Embedded Systems and Smartphones. MSWIM '18 21st ACM Int'l Conference on Modelling, Analysis and Simulation of Wireless and Mobile Systems, Oct 2018, Montreal, Canada. ⟨10.1145/3242102.3242139⟩. ⟨hal-01907145⟩