Leveraging Energy Cycle Regularity to Predict Adaptive Mode for Non-volatile Processors

Ambient energy harvesting technique is currently an ideal alternative to the state-of-the-art batteries for the power supply of IoT edge devices. Due to the intermittent power supply of the ambient energy, the systems suffer data loss and procedure rollbacks. NVPs have been proposed to ameliorate this problem through storing the volatile data into NVM when power fails and coping back them when power resumes. Recent studies have shown that NVPs can enter the retention mode when a power failure occurs so as to further mitigate the backup and recovery overheads through waiting for power resumption instead of immediate backup. However, the effectiveness of retention-based mechanism highly depends on energy prediction, which usually results in a complicated and slow mode decision process. If we use simple mode decision mechanism, the system may often enter inappropriate mode. In this work, we observe an interesting phenomenon that quite a few ambient energy waveforms exhibit regularity that the duration of the power outage in one energy cycle is quite akin to the adjacent ones. Addressing the mode decision issue upon power failures and exploiting the power regularity property, we build a fast history adaptive mechanism to accurately determine the backup or retention modes for a NVP system upon power dropping to a threshold. The metrics of energy cycle length, historical mode ratio and resumption time are defined to direct the proposed two-phase mode decision process. Experimental evaluations demonstrate the proposed prediction mechanism achieves up to 1.38X execution progress and up to 41.9% improvement on energy utilization over the conventional scheme.