A framework for detecting energy bugs in smartphones

Abbasi, Abdul Muqtadir; Al‐tekreeti, Mustafa; Ali, Yasir; Naik, Kshirasagar; Nayak, Amiya; Goel, Nikunj; Plourde, Bernard

doi:10.1109/nof.2015.7333297

Cited by 3 publications

(1 citation statement)

References 3 publications

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“…The interest in power consumption went even further than just improving efficiency. Researchers have studied power consumption to detect and solve energy bugs [13], create energy-aware software [14], and even to creatively deal with cyber-security issues [15,16] and detect anomalies [17,18].…”

Section: Introductionmentioning

confidence: 99%

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

Djedidi

Djeziri

2020

Journal of Systems Architecture

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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%).

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