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Adaptive Power monitoring for self-aware embedded systems

Mohamad El Ahmad 1 Mohamad Najem 1 Pascal Benoit 1 Gilles Sassatelli 1 Lionel Torres
1 ADAC - ADAptive Computing
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Dynamic Thermal and Power Management methods require efficient monitoring techniques. Based on a set of data collected by sensors, embedded models estimate online the power consumption: this task is a real challenge, since models must be both accurate and low cost, but they also have to be robust to variations. In this paper, we investigate a self-aware approach using the performance events and the external temperature. We present a solution (PESel) for the selection of the relevant information. This method is two times faster than existing solutions and provides better results compared to related works. The power models achieve a 96% accuracy with a temporal resolution of 100 ms, and negligible performance/energy overheads (less than 1%). Moreover, we show that our estimations are not sensitive to external temperature variations.
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Submitted on : Sunday, January 17, 2016 - 4:10:06 PM
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Mohamad El Ahmad, Mohamad Najem, Pascal Benoit, Gilles Sassatelli, Lionel Torres. Adaptive Power monitoring for self-aware embedded systems. NORCAS: Nordic Circuits and Systems Conference, Oct 2015, Oslo, Norway. ⟨10.1109/NORCHIP.2015.7364364⟩. ⟨lirmm-01257519⟩



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