# Deep learning with coherent nanophotonic circuits

TL;DR: A new architecture for a fully optical neural network is demonstrated that enables a computational speed enhancement of at least two orders of magnitude and three order of magnitude in power efficiency over state-of-the-art electronics.

Abstract: Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.

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##### Citations

^{1}, Harvard University

^{2}, Bell Labs

^{3}, University of Bordeaux

^{4}, University of Washington

^{5}

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##### References

73,871 citations

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^{1}, Facebook

^{2}, Université de Montréal

^{3}, Google

^{4}, University of Toronto

^{5}

33,931 citations

^{1}

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### "Deep learning with coherent nanopho..." refers background or methods in this paper

...The computational resolution of ONNs is limited by practical non-idealities, including (1) thermal crosstalk between phase shifters in interferometers, (2) optical coupling drift, (3) the finite precision with which an optical phase can be set (16 bits in our case), (4) photodetection noise and (5) finite photodetection dynamic range (30 dB in our case)....

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...(3) Once a neural network is trained, the architecture can be passive, and computation on the optical signals will be performed without additional energy input....

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...We used four instances of the OIU to realize the following matrix transformations in the spatial-mode basis: (1) U((1))Σ((1)), (2) V((1)), (3) U((2))Σ((2)) and (4) V((2))....

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...Transformations (1) and (2) realize the first matrix M((1)), and (3) and (4) implement M((2))....

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