Exploring the Effect of Energy Storage Sizing on Intermittent Computing System Performance

Abstract

Batteryless energy-harvesting devices promise to deliver a sustainable Internet of Things. Intermittent computing is an emerging area, where the application forward progress, i.e., computation beneficial to the progress of the active application, is maintained by saving the volatile computing state into nonvolatile memory before power interruptions, and restored afterward. Conventional intermittent computing approaches typically minimize energy storage to reduce device dimensions and interruption periods, but this can result in high state-saving and -restoring overheads and impede forward progress. In this article, we argue that adding a small amount of energy storage can significantly improve the forward progress. We develop an intermittent computing model that accurately estimates the forward progress, with an experimentally validated mean error of 0.5%. Using this model, we show that sizing energy storage can improve the forward progress by up to 65% with a constant current supply, and 43% with real-world photovoltaic sources. An extension to this approach, which uses a cost function to trade off the energy storage size against forward progress, can save 83% of capacitor volume and 91% of interruption periods while maintaining 93% of the maximum forward progress.