  This work aims to enable persistent, event-driven sensing and decision
capabilities for energy-harvesting (EH)-powered devices by deploying
lightweight DNNs onto EH-powered devices. However, harvested energy is usually
weak and unpredictable and even lightweight DNNs take multiple power cycles to
finish one inference. To eliminate the indefinite long wait to accumulate
energy for one inference and to optimize the accuracy, we developed a power
trace-aware and exit-guided network compression algorithm to compress and
deploy multi-exit neural networks to EH-powered microcontrollers (MCUs) and
select exits during execution according to available energy. The experimental
results show superior accuracy and latency compared with state-of-the-art
techniques.
