  In this research, we propose a tiny image segmentation model, L^3U-net, that
works on low-resource edge devices in real-time. We introduce a data folding
technique that reduces inference latency by leveraging the parallel
convolutional layer processing capability of the CNN accelerators. We also
deploy the proposed model to such a device, MAX78000, and the results show that
L^3U-net achieves more than 90% accuracy over two different segmentation
datasets with 10 fps.
