  Advances in low-power electronics and machine learning techniques lead to
many novel wearable IoT devices. These devices have limited battery capacity
and computational power. Thus, energy harvesting from ambient sources is a
promising solution to power these low-energy wearable devices. They need to
manage the harvested energy optimally to achieve energy-neutral operation,
which eliminates recharging requirements. Optimal energy management is a
challenging task due to the dynamic nature of the harvested energy and the
battery energy constraints of the target device. To address this challenge, we
present a reinforcement learning-based energy management framework, tinyMAN,
for resource-constrained wearable IoT devices. The framework maximizes the
utilization of the target device under dynamic energy harvesting patterns and
battery constraints. Moreover, tinyMAN does not rely on forecasts of the
harvested energy which makes it a prediction-free approach. We deployed tinyMAN
on a wearable device prototype using TensorFlow Lite for Micro thanks to its
small memory footprint of less than 100 KB. Our evaluations show that tinyMAN
achieves less than 2.36 ms and 27.75 $\mu$J while maintaining up to 45% higher
utility compared to prior approaches.
