  A fundamental challenge for running machine learning algorithms on
battery-powered devices is the time and energy limitations, as these devices
have constraints on resources. There are resource-efficient classifier
algorithms that can run on these devices, but their accuracy is often
sacrificed for resource efficiency. Here, we propose an ultra-low power
classifier, SEFR, with linear time complexity, both in the training and the
testing phases. SEFR is comparable to state-of-the-art classifiers in terms of
classification accuracy, but it is 63 times faster and 70 times more energy
efficient than the average of state-of-the-art and baseline classifiers on
binary class datasets. The energy and memory consumption of SEFR is very
insignificant, and it can even perform both train and test phases on
microcontrollers. To our knowledge, this is the first multipurpose
classification algorithm specifically designed to perform both training and
testing on ultra-low power devices.
