@inproceedings{b15d01d92c054d34bb91b007abf72bc7,
title = "Performance evaluation of hybrid ANN based time series prediction on embedded processor",
abstract = "Complex embedded systems application exhibit time-varying workload which requires continuous resource adaptivity. Workload prediction has been successfully achieved through a hybrid model of NARX Recurrent Neural Networks combined with Self Organizing Map (SOM). This paper presents the performance evaluation of this hybrid time series prediction on embedded processors as an alternative to dedicated hardware. Achieved results demonstrate the potential of this approach for heavy workloads such as parallel applications. This solution is prone to extension to MPSOC.",
keywords = "ANN, MPSOC, SOC, embedded, processor, time series",
author = "Possignolo, \{Rafael Trapani\} and Omar Hammami",
note = "Publisher Copyright: {\textcopyright} 2010 IEEE.; 1st IEEE Latin American Symposium on Circuits and Systems, LASCAS 2010 ; Conference date: 24-02-2010 Through 26-02-2010",
year = "2016",
month = feb,
day = "17",
doi = "10.1109/LASCAS.2010.7410246",
language = "English",
series = "Proceedings - 2010 1st IEEE Latin American Symposium on Circuits and Systems, LASCAS 2010",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "204--207",
booktitle = "Proceedings - 2010 1st IEEE Latin American Symposium on Circuits and Systems, LASCAS 2010",
}