  Energy harvesting (EH) IoT devices that operate intermittently without
batteries, coupled with advances in deep neural networks (DNNs), have opened up
new opportunities for enabling sustainable smart applications. Nevertheless,
implementing those computation and memory-intensive intelligent algorithms on
EH devices is extremely difficult due to the challenges of limited resources
and intermittent power supply that causes frequent failures. To address those
challenges, this paper proposes a methodology that enables fast deep learning
with low-energy accelerators for tiny energy harvesting devices. We first
propose $RAD$, a resource-aware structured DNN training framework, which
employs block circulant matrix and structured pruning to achieve high
compression for leveraging the advantage of various vector operation
accelerators. A DNN implementation method, $ACE$, is then proposed that employs
low-energy accelerators to profit maximum performance with small energy
consumption. Finally, we further design $FLEX$, the system support for
intermittent computation in energy harvesting situations. Experimental results
from three different DNN models demonstrate that $RAD$, $ACE$, and $FLEX$ can
enable fast and correct inference on energy harvesting devices with up to 4.26X
runtime reduction, up to 7.7X energy reduction with higher accuracy over the
state-of-the-art.
