Conference papers
A Novel Easy-to-construct Power Model for Embedded and Mobile Systems: Using Recursive Neural Nets to Estimate Power Consumption of ARM-based Embedded Systems and Mobile Devices
Abstract : This paper features a novel modeling scheme for power consumption in embedded and mobile devices. The model hereafter presented is built thought data fitting techniques using a NARX nonlinear neural net. It showcases the advantages of using a nonlinear model to estimate power consumption over the widely used linear regression models, where The NARX neural net is simpler, easier to implement, and more importantly more suitable as power changes are not always linear. Finally, experimental results validate the model with one with an accuracy of 97.1% on a smartphone.
Cited literature [34 references]
https://hal-amu.archives-ouvertes.fr/hal-01856579
Contributor : Oussama Djedidi Connect in order to contact the contributor
Submitted on : Friday, August 17, 2018 - 7:26:59 PM
Last modification on : Monday, January 17, 2022 - 12:40:05 AM
Long-term archiving on: : Sunday, November 18, 2018 - 12:42:49 PM
Contributor : Oussama Djedidi Connect in order to contact the contributor
Submitted on : Friday, August 17, 2018 - 7:26:59 PM
Last modification on : Monday, January 17, 2022 - 12:40:05 AM
Long-term archiving on: : Sunday, November 18, 2018 - 12:42:49 PM
File
ICINCO_2018_209.pdf
Explicit agreement for this submission