Fine-Grained Monitoring For Self-Aware Embedded Systems

Mohamad Najem 1 Mohamad El Ahmad 1 Pascal Benoit 1 Gilles Sassatelli 1 Lionel Torres 1
1 ADAC - ADAptive Computing
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Dynamic Thermal and Power Management methods highly depend on the quality of the monitoring, which needs to provide estimations of the system's state. This can be achieved with a set of performance counters that can be configured to track logical events at different levels. Although this problem has been addressed in the literature, recently developed highly reactive adaptation techniques require faster, more accurate and more robust estimations methods. A systematic approach (PESel) is proposed for the selection of the relevant performance events from the local, shared and system resources. We investigate an implementation of a neural network based estimation technique which provides better results compared to related works. Our approach is robust to external temperature variations and takes into account dynamic scaling of the operating frequency. It achieves 96% accuracy with a temporal resolution of 100 ms, with negligible performance/energy overheads (less than 1%).
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Article dans une revue
Microprocessors and Microsystems: Embedded Hardware Design (MICPRO), Elsevier, 2017, 48, pp.3-10. 〈10.1016/j.micpro.2016.09.004〉
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https://hal-lirmm.ccsd.cnrs.fr/lirmm-01384558
Contributeur : Mohamad El Ahmad <>
Soumis le : jeudi 20 octobre 2016 - 10:53:57
Dernière modification le : vendredi 25 mai 2018 - 01:40:53

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Mohamad Najem, Mohamad El Ahmad, Pascal Benoit, Gilles Sassatelli, Lionel Torres. Fine-Grained Monitoring For Self-Aware Embedded Systems. Microprocessors and Microsystems: Embedded Hardware Design (MICPRO), Elsevier, 2017, 48, pp.3-10. 〈10.1016/j.micpro.2016.09.004〉. 〈lirmm-01384558〉

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