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This paper introduces intermittent learning - the goal of which is to enableenergy harvested computing platforms capable of executing certain classes ofmachine learning tasks effectively and efficiently. We identify uniquechallenges to intermittent learning relating to the data and applicationsemantics of machine learning tasks, and to address these challenges, we devise1) an algorithm that determines a sequence of actions to achieve the desiredlearning objective under tight energy constraints, and 2) propose threeheuristics that help an intermittent learner decide whether to learn or discardtraining examples at run-time which increases the energy efficiency of thesystem. We implement and evaluate three intermittent learning applications thatlearn the 1) air quality, 2) human presence, and 3) vibration using solar, RF,and kinetic energy harvesters, respectively. We demonstrate that the proposedframework improves the energy efficiency of a learner by up to 100% and cutsdown the number of learning examples by up to 50% when compared tostate-of-the-art intermittent computing systems that do not implement theproposed intermittent learning framework.

Intermittent Learning: On-Device Machine Learning on Intermittently Powered System