Dual-threshold directed execution progress maximization for nonvolatile processors

Abstract

To meet the needs of the Internet of Things (IoTs) devices, energy harvesting systems are proposed to power the systems instead of battery. Addressing the problem that harvested energy is unstable, nonvolatile processors (NVPs) have been proposed to hold intermediate data and avoid frequent program restarting from the beginning. However, NVPs often suffer a lot of waste on energy and system sources that can not be used for program execution owing to the frequent backup and recovery operations. To further improve the performance of NVPs, the paper proposes a dual-threshold method to maximize execution progress by enabling a system to hibernate to wait for power resumption instead of backing up data directly upon power interruptions. In particular, the optimal high and low thresholds, and the switches of system hibernation and backup, are discussed in details in order to achieve the goal of maximizing computation progress. The evaluation results show an average of up to 82.3% reduction on power failures and 1.5x speedup for forwarding progress by the proposed dual-threshold method compared to the conventional single threshold scheme.