Power estimation model for fall detection system

Abstract

In today\'s high-performance system designs, power consideration is becoming increasingly important,leading to the development of the power consumption in MPSoC at an early step of the design. The aim of our work is to define a methodology to explore power estimation model for Fall Detection System. New power consumption models for processor core are defined according to different configurations of the target architecture (Zynq-7000 AP SoC platform) and the features of the applications like core frequency, number of processor cores, image resolution. Functional Level Power Analysis method is also applied to the extraction of the power models. In our work, we target accuracy based modeling style and analysis of information collected from measurement on real board to obtain sufficiently accurate power estimation for the Fall Detection System on a heterogeneous platform. Besides, to validate the accuracy of the proposed models, we also analyze the average error rate of these models that show that object segmentation (2.2%), Filter (2.4%), Feature Extraction (3.5%), and Recognition (2.9%).