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• W2977468072 abstract "Photonic neuromorphic computing is raising a growing interest as it promises to provide massive parallelism and low power consumption. In this paper, we demonstrate for the first time a feed-forward neural network via an 8 × 8 Indium Phosphide cross-connect chip, where up to 8 on-chip weighted addition circuits are co-integrated, based on semiconductor optical amplifier technology. We perform the weight calibration per neuron, resulting in a normalized root mean square error smaller than 0.08 and a best case dynamic range of 27 dB. The 4 input to 1 output weighted addition operation is executed on-chip and is part of a neuron, whose non-linear function is implemented via software. A three feedback loop optimization procedure is demonstrated to enable an output neuron accuracy improvement of up to 55%. The exploitation of this technology as neural network is evaluated by implementing a trained 3-layer photonic deep neural network to solve the Iris flower classification problem. Prediction accuracy of 85.8% is achieved, with respect to the 95% accuracy obtained via a computer. A comprehensive analysis of the error evolution in our system reveals that the electrical/optical conversions dominate the error contribution, which suggests that an all optical approach is preferable for future neuromorphic computing hardware design." @default.
• W2977468072 created "2019-10-10" @default.
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• W2977468072 date "2020-01-01" @default.
• W2977468072 modified "2023-10-14" @default.
• W2977468072 title "Deep Neural Network Through an InP SOA-Based Photonic Integrated Cross-Connect" @default.
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• W2977468072 doi "https://doi.org/10.1109/jstqe.2019.2945548" @default.
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