Adaptive Power monitoring for self-aware embedded systems

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.