Abstract
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. Here we demonstrate systematic improvements in diffractive optical neural networks based on a differential measurement technique that mitigates the non-negativity constraint of light intensity. In this scheme, each class is assigned to a separate pair of photodetectors, behind a diffractive network, and the class inference is made by maximizing the normalized signal difference between the detector pairs. Moreover, by utilizing the inherent parallelization capability of optical systems, we reduced the signal coupling between the positive and negative detectors of each class by dividing their 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 among multiple jointly-trained differential diffractive neural networks. Using this class-specific differential detection in jointly-optimized diffractive networks, our simulations achieved testing accuracies of 98.52%, 91.48% and 50.82% for MNIST, Fashion-MNIST and grayscale CIFAR-10 datasets, respectively. Similar to ensemble methods practiced in machine learning, we also independently-optimized multiple differential diffractive networks that optically project their light onto a common detector plane, and achieved testing accuracies of 98.59%, 91.06% and 51.44% for MNIST, Fashion-MNIST and grayscale CIFAR-10, respectively. Through these systematic advances in designing diffractive neural networks, the reported classification accuracies set the state-of-the-art for an all-optical neural network design.
Highlights
Machine learning, and in particular deep learning, has drastically impacted the area of information and data processing in recent years.[1,2,3,4,5] Research on optical machine learning has a very rich history,[6,7,8,9,10,11,12] due to its advantages in terms of power efficiency, scalability, computational capacity, and speed
We introduced diffractive deep neural networks,[25,27] which are composed of successive diffractive optical layers, trained and designed using deep learning methods in a computer, and physically fabricated to all-optically perform statistical inference based on the trained task at hand
For the sake of clarity, we devised a notation to represent these different optical network designs based on (1) the number of positive and negative detectors at the output plane, (2) the number of jointly trained but independent diffractive networks, (3) the number of layers constituting each one of these individual diffractive neural networks, and (4) the number of neurons at each diffractive layer of an individual optical network
Summary
In particular deep learning, has drastically impacted the area of information and data processing in recent years.[1,2,3,4,5] Research on optical machine learning has a very rich history,[6,7,8,9,10,11,12] due to its advantages in terms of power efficiency, scalability, computational capacity, and speed. We introduced diffractive deep neural networks,[25,27] which are composed of successive diffractive optical layers (transmissive and/or reflective), trained and designed using deep learning methods in a computer, and physically fabricated to all-optically perform statistical inference based on the trained task at hand In this framework, complex wave field of a given scene or object, illuminated by a coherent light source, propagates through the diffractive layers, which collectively modulate the propagating light such that the intensity at the output plane of the diffractive network is distributed in a desired way; i.e., based on the specific classification or imaging task of interest, these diffractive layers jointly determine the output plane intensity in response to an input. Because of the passive nature of diffractive neural networks, at the cost of optical set-up alignment complexity as well as illumination power increase, one can create scalable, low-power, and competitive solutions to perform optical computation and machine learning through these jointly optimized diffractive neural network systems
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
