Deep neural network through an InP SOA-based photonic integrated cross-connect

Bin Shi (Corresponding author), Nicola Calabretta, Ripalta Stabile

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

28 Citaten (Scopus)
12 Downloads (Pure)


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.
Originele taal-2Engels
Aantal pagina's11
TijdschriftIEEE Journal of Selected Topics in Quantum Electronics
Nummer van het tijdschrift1
StatusGepubliceerd - 1 jan 2020


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