Fat-U-Net: Non-Contracting U-Net for Free-Space Optical Neural Networks

Abstract

This paper describes the advantages and disadvantages of adapting the U-Net architecture from a traditional GPU to a 4f free-space optical environment. The implementation is based on an optical-based acceleration called FatNet and thus this adaption is called Fat-U-Net. Fat-U-Net neglects the pooling operations in UNet, but maintains a similar number of weights and pixels per layer as U-Net. Our results demonstrate that the conversion to Fat-U-Net offers significant improvement in speed for segmentation tasks, with Fat-U-Net achieving a remarkable ×538 acceleration in inference compared to U-Net when both are run on optical devices and x37 acceleration in inference compared to the results provided by U-Net on GPU. The performance loss after conversion remains minimal in two datasets, with reductions of 4.24% in IoU for the Oxford IIIt pet dataset and 1.76% in IoU of HeLa cells nucleus segmentation.