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

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

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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.
Original languageEnglish
Article number8859353
Number of pages11
JournalIEEE Journal of Selected Topics in Quantum Electronics
Volume26
Issue number1
DOIs
Publication statusPublished - 1 Jan 2020

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Keywords

  • Artificial neural networks
  • image classification
  • photonic integrated circuits
  • semiconductor optical amplifiers
  • Semiconductor optical amplifiers
  • Neurons
  • Wavelength division multiplexing
  • Biological neural networks
  • Indium phosphide
  • Arrayed waveguide gratings
  • Photonics

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