Design framework for metasurface optics-based convolutional neural networks
Carlos Mauricio Villegas Burgos, Tianqi Yang, Yuhao Zhu, A. Nickolas Vamivakas
Abstract
Deep learning using convolutional neural networks (CNNs) has been shown to significantly outperform many conventional vision algorithms. Despite efforts to increase the CNN efficiency both algorithmically and with specialized hardware, deep learning remains difficult to deploy in resource-constrained environments. In this paper, we propose an end-to-end framework to explore how to optically compute the CNNs in free-space, much like a computational camera. Compared to existing free-space optics-based approaches that are limited to processing single-channel (i.e., gray scale) inputs, we propose the first general approach, based on nanoscale metasurface optics, that can process RGB input data. Our system achieves up to an order of magnitude energy savings and simplifies the sensor design, all the while sacrificing little network accuracy.