Deep learning with coherent nanophotonic circuits
Citations
32 citations
32 citations
32 citations
Cites methods from "Deep learning with coherent nanopho..."
...Shen et al proposed a theoretical fully optical neural network architecture where each layer of the network is composed of an optical interference unit (OIU) to perform the linear matrix multiplication and an optical nonlinear unit (ONU) that acts as the nonlinear activation (figure 9(a)) [97]....
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...They experimentally demonstrated that this system is capable of vowel recognition with an accuracy comparable of that of a conventional digital computer [95]....
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...So far, two different approaches have been used for the physical realization of photonic networks: the first was suggested by Shen et al [95] and relied on nanophotonic circuits; the other proposed by Lin et al [96] is based on diffractive optical elements....
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32 citations
32 citations
Cites background from "Deep learning with coherent nanopho..."
...Perhaps among the most promising solutions for providing hardware implementations of deep neural networks is using photonics [11]....
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...These provides significant advantages over the currently used solutions, often outperforming them by several order of magnitude [11]....
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References
73,978 citations
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23,074 citations
"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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16,717 citations
14,635 citations
"Deep learning with coherent nanopho..." refers methods in this paper
...ANNs can be trained by feeding training data into the input layer and then computing the output by forward propagation; weighting parameters in each matrix are subsequently optimized using back propagation [16]....
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