Class-specific differential detection in diffractive optical neural networks improves inference accuracy  

作　　者：Jingxi Li Deniz Mengu Yi Luo Yair Rivenson Aydogan Ozcan 

机构地区：[1]University of California at Los Angeles,Department of Electrical and Computer Engineering,Los Angeles,California,United States [2]University of California at Los Angeles,Department of Bioengineering,Los Angeles,California,United States [3]University of California at Los Angeles,California NanoSystems Institute,Los Angeles,California,United States

出　　处：《Advanced Photonics》2019年第4期2-14,共13页先进光子学（英文）

摘　　要：Optical computing provides unique opportunities in terms of parallelization,scalability,power efficiency,and computational speed and has attracted major interest for machine learning.Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference,achieving promising performance for object classification and imaging.We demonstrate systematic improvements in diffractive optical neural networks,based on a differential measurement technique that mitigates the strict nonnegativity constraint of light intensity.In this differential detection scheme,each class is assigned to a separate pair of detectors,behind a diffractive optical network,and the class inference is made by maximizing the normalized signal difference between the photodetector pairs.Using this differential detection scheme,involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons,we numerically achieved blind testing accuracies of 98.54%,90.54%,and 48.51%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively.Moreover,by utilizing the inherent parallelization capability of optical systems,we reduced the cross-talk and optical signal coupling between the positive and negative detectors of each class by dividing the optical path into two jointly trained diffractive neural networks that work in parallel.We further made use of this parallelization approach and divided individual classes in a target dataset among multiple jointly trained diffractive neural networks.Using this class-specific differential detection in jointly optimized diffractive neural networks that operate in parallel,our simulations achieved blind testing accuracies of 98.52%,91.48%,and 50.82%for MNIST,Fashion-MNIST,and grayscale CIFAR-10 datasets,respectively,coming close to the performance of some of the earlier generations of all-electronic deep neural networks,e.g.,LeNet,which achieves classif

关 键 词：optical computation optical neural networks deep learning optical machine learning diffractive deep neural networks 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O436.1[自动化与计算机技术—控制科学与工程]