A run-time, feedback-based energy estimation model For embedded devices

We present an adaptive, feedback-based, energy estimation model for battery-powered embedded devices such as sensor network gate-ways and hand-held computers. Our technique maps hardware and software counters to energy consumption values using a set of first order, linear regression equations. Our system is novel in that it combines online and offline techniques to enable runtime power prediction. Our system employs an offline instantiated model that it continuously updates using feedback from a readily available battery monitor within the device. We empirically evaluate our model and detail its robustness, accuracy, and computational cost. We also analyze the stability of the model in the presence of feedback errors. We demonstrate that our approach can achieve an error rate of 1% (extant techniques: 2.6% to 4%) for computationally bound tasks and 6.6% (extant techniques: 11%) for communication bound tasks.