Deep learning with coherent nanophotonic circuits
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...tem performance. On the basic research side, optical neuromorphic accelerators are being intensively investigated as candidates for the DNN processor. They typically use a Mach-Zehnder interferometer [37] or a discrete diffractive optical element [30, 5, 3] as a weight element. Adjoint optimization of these elements has already been shown in [30, 3, 20]. In general, increasing the number of nodes for ...
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... structure; e.g. compressed sensing, computational imaging [39, 11, 6], and optical communication [19]. Another application is an optical processor for ultrafast and energy-efficient inference engines [37]. This is because the operation of the the transferred network is performed at the speed of light, which does not require any principal energy consumption Scalability of physical SE-NET: Our SE-NET-ba...
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...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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...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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