All-optical neural network promises faster future AI

September 05, 2019 //By Julien Happich
neural networks
Researchers at The Hong Kong University of Science and Technology have demonstrated a multilayer all-optical artificial neural network which they prove was scalable and easily reconfigurable.

The research published in Optica under the title “All-optical neural network with non-linear activation functions” discloses a fully built All-Optical Neural Network (AONN) in which linear operations were programmed by spatial light modulators and Fourier lenses, while non-linear optical activation functions were realized based on electromagnetically induced transparency (EIT), a light-induced quantum interference effect among atomic transitions.

Schematic of experimental realization of an optical neuron
including linear and nonlinear operations.

“This light-induced effect can be achieved with very weak laser power,” noted lead professor Shengwang Du. Because this effect is based on non-linear quantum interference, the researchers expect such a system could be extended to a quantum neural network that would be able to solve problems that could not be solved by classical methods.

In their paper, the researchers report a proof-of-concept two-layer, fully connected AONN with 16 inputs and two outputs. The team used its all-optical network to classify the order and disorder phases of the Ising model, a statistical model of magnetism. The results showed that the all-optical neural network was as accurate as a well-trained computer-based neural network.

“Our all-optical scheme could enable a neural network that performs optical parallel computation at the speed of light while consuming little energy,” co-author Junwei Liu said.

Moreover, the authors note that because the linear matrix elements and non-linear functions can be independently programmed, one system could easily be reconfigured to realize different AONN architectures, without modify its physical structure.

Next, the researchers plan to expand their all-optical approach to large-scale all-optical deep neural networks with complex architectures designed for specific applications such as image recognition.

Hong Kong University of Science and Technology -

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