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%).

电子系统中的功耗是能源自治管理，性能分析和组件老化监视的基本功能。因此，一些研究致力于开发嵌入式系统的功率模型和分析器。这些模型中的每一个都旨在适应特定的使用环境。本文是致力于对机载设备中的嵌入式系统进行建模和监视的一系列工作的一部分。本文的目的是双重的。首先，它概述了文献中最常用的模型。然后，它根据一组标准（例如建模假设，必要的必要仪器，准确性和实现的复杂性）对这些模型进行比较分析。

其次，我们引入了具有组件级粒度的基于ARM的嵌入式系统的新功耗估算器。估计器基于NARX神经网络，用于监视功率以进行诊断。获得的实验结果凸显了文献中提出的模型的优点和局限性，并证明了拟议的NARX的有效性，该NARX在同类智能手机中获得了最佳的结果（在线平均绝对百分比误差= 2.2％）。