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Sensing systems powered by energy harvesting have traditionally been designedto tolerate long periods without energy. As the Internet of Things (IoT)evolves towards a more transient and opportunistic execution paradigm, reducingenergy storage costs will be key for its economic and ecologic viability.However, decreasing energy storage in harvesting systems introduces reliabilityissues. Transducers only produce intermittent energy at low voltage and currentlevels, making guaranteed task completion a challenge. Existing ad hoc methodsovercome this by buffering enough energy either for single tasks, incurringlarge data-retention overheads, or for one full application cycle, requiring alarge energy buffer. We present Julienning: an automated method for optimizingthe total energy cost of batteryless applications. Using a custom specificationmodel, developers can describe transient applications as a set of atomicallyexecuted kernels with explicit data dependencies. Our optimization flow canpartition data- and energy-intensive applications into multiple executioncycles with bounded energy consumption. By leveraging interkernel datadependencies, these energy-bounded execution cycles minimize the number ofsystem activations and nonvolatile data transfers, and thus the total energyoverhead. We validate our methodology with two batteryless cameras runningenergy-intensive machine learning applications. Results demonstrate thatcompared to ad hoc solutions, our method can reduce the required energy storageby over 94% while only incurring a 0.12% energy overhead.

Memory-Aware Partitioning of Machine Learning Applications for Optimal Energy Use in Batteryless Systems