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• W4285090977 abstract "We propose and experimentally demonstrate an optical processor for a binarized neural network (NN). Implementation of a binarized NN involves multiply-accumulate operations, in which positive and negative weights should be implemented. In the proposed processor, the positive and negative weights are realized by switching the operations of a dual-drive Mach–Zehnder modulator (DD-MZM) between two quadrature points corresponding to two binary weights of +1 and −1, and the multiplication is also performed at the DD-MZM. The accumulation operation is realized by dispersion-induced time delays and detection at a photodetector (PD). A proof-of-concept experiment is performed. A binarized convolutional neural network (CNN) accelerated by the optical processor at a speed of 32 giga floating point operations/s (GFLOPS) is tested on two benchmark image classification tasks. The large bandwidth and parallel processing capability of the processor has high potential for next generation data computing." @default.
• W4285090977 created "2022-07-14" @default.
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• W4285090977 date "2022-07-27" @default.
• W4285090977 modified "2023-10-18" @default.
• W4285090977 title "Optical processor for a binarized neural network" @default.
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• W4285090977 doi "https://doi.org/10.1364/ol.464214" @default.
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