All-optical machine learning using diffractive deep neural networks | TDLS

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Toronto Deep Learning Series, 10 September 2018

For slides and more information, visit https://tdls.a-i.science/events/2018-09-10/

Paper Review: http://innovate.ee.ucla.edu/wp-content/uploads/2018/07/2018-optical-ml-neural-network.pdf

Speaker: https://www.linkedin.com/in/russell-pollari-b555895a/
Organizer: https://www.linkedin.com/in/amirfz/

Host: Rangle.io

Paper abstract:
Deep learning has been transforming our ability to execute advanced inference tasks using computers. We introduce a physical mechanism to perform machine learning by demonstrating an all-optical Diffractive Deep Neural Network (D2NN) architecture that can implement various functions following the deep learning-based design of passive diffractive layers that work collectively. We create 3D-printed D2NNs that implement classification of images of handwritten digits and fashion products as well as the function of an imaging lens at terahertz spectrum. Our all-optical deep learning framework can perform, at the speed of light, various complex functions that computer-based neural networks can implement, and will find applications in all-optical image analysis, feature detection and object classification, also enabling new camera designs and optical components that perform unique tasks using D2NNs.



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Tags:
neural net
optics
optical neural networks
quantum machine learning
diffractive deep neural networks
deep learning