Neural Network-based Prediction Algorithms for In-Door Multi-Source Energy Harvesting System for Non-Volatile Processors

Abstract

Due to size, longevity, safety, and recharging concerns, energy harvesting is becoming a better choice for many wearable embedded systems than batteries. However, harvested energy is intrinsically unstable. In order to overcome this drawback, non-volatile processors (NVPs) have been proposed to bridge intermittent program execution. However, even with NVPs, frequent power interruptions will severely degrade system performance. Hence, in this paper we adopt a multi-source in-door energy harvesting architecture to compensate the shortcoming of single energy source. We further investigate power harvesting prediction techniques, which are critical for NVP systems since they can coordinate with task scheduler in the NVP system to compensate the intermittent ambient energy harvesting. We investigate prediction methods both for single energy harvesting source and for multiple energy harvesting sources, the total output power of which is more stable compared with the single source case. A comprehensive evaluation framework has been developed using actually measured harvesting traces on the proposed neural network-based power harvesting prediction methods. It turns out that the most favorable prediction methods are directly predicting the total output power of DC-DC converters (connecting between energy sources and NVP), or predicting the total input power of DC-DC converters first and then inferring the total output power using a learned mapping function, for multi-source power harvesting predictions.