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3D-Printed Deep Learning Neural Network Uses Light Instead of Electrons (newatlas.com)
Matt Kennedy from New Atlas reports of an all-optical Diffractive Deep Neural Network (D2NN) architecture that uses light diffracted through numerous plates instead of electrons. It was developed by Dr. Aydogan Ozcan and his team of researchers at the Chancellor's Professor of electrical and computer engineering at UCLA. From the report: The setup uses 3D-printed translucent sheets, each with thousands of raised pixels, which deflect light through each panel in order to perform set tasks. By the way, these tasks are performed without the use of any power, except for the input light beam. The UCLA team's all-optical deep neural network -- which looks like the guts of a solid gold car battery -- literally operates at the speed of light, and will find applications in image analysis, feature detection and object classification. Researchers on the team also envisage possibilities for D2NN architectures performing specialized tasks in cameras. Perhaps your next DSLR might identify your subjects on the fly and post the tagged image to your Facebook timeline. For now though, this is a proof of concept, but it shines a light on some unique opportunities for the machine learning industry. The research has been published in the journal Science.
and the "magazine" article, get the research paper straight from the horse's mouth for free.
http://innovate.ee.ucla.edu/wp...
The impressive part of the work is the fact that they were able to print materials that control light so well, not the actual network itself. The weight optimization and topology design were still done using standard computing hardware - this is just a physical realization of a trained network. You could build something equivalent out of water and tubes (though it would be slower and wetter obviously). The cool part is the optical control which is now possible, not the fact that MNIST works.