Deep learning with coherent nanophotonic circuits
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Cites methods from "Deep learning with coherent nanopho..."
...Depending on the application of interest, the DMM section can be used to extend the 4 × 4 optical processor to a larger structure by cascading it with another SU(4), as in [6] to perform SVD optically, or in [3] for deep-learning matrix multiplications in neural networks applications....
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...In this regard, the phase shifters in such a mesh of MZIs are employed for its simple experimental calibration, which makes the structure suitable candidate to serve as a reconfigurable linear optical processor [1]–[3]....
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...This paper presents the theoretical and experimental analysis of a 4 × 4 reconfigurable MZI-based optical processor that can be configured for a given application to perform linear functions, such as matrix multiplications in computational systems like optical neural networks [3], [4] quantum transport simulations [5],...
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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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"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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