  We propose Zygarde -- which is an energy -- and accuracy-aware soft real-time
task scheduling framework for batteryless systems that flexibly execute deep
learning tasks1 that are suitable for running on microcontrollers. The sporadic
nature of harvested energy, resource constraints of the embedded platform, and
the computational demand of deep neural networks (DNNs) pose a unique and
challenging real-time scheduling problem for which no solutions have been
proposed in the literature. We empirically study the problem and model the
energy harvesting pattern as well as the trade-off between the accuracy and
execution of a DNN. We develop an imprecise computing-based scheduling
algorithm that improves the timeliness of DNN tasks on intermittently powered
systems. We evaluate Zygarde using four standard datasets as well as by
deploying it in six real-life applications involving audio and camera sensor
systems. Results show that Zygarde decreases the execution time by up to 26%
and schedules 9%-34% more tasks with up to 21% higher inference accuracy,
compared to traditional schedulers such as the earliest deadline first (EDF).
