Photonic pattern reconstruction enabled by on-chip online learning and inference

Bicky A. Marquez, Zhimu Guo, Hugh Morison, Sudip Shekhar, Lukas Chrostowski, Paul Prucnal, Bhavin J. Shastri

Research output: Contribution to journalArticlepeer-review

Abstract

Recent investigations in neuromorphic photonics exploit optical device physics for neuron models, and optical interconnects for distributed, parallel, and analog processing. Integrated solutions enabled by silicon photonics enable high-bandwidth, low-latency and low switching energy, making it a promising candidate for special-purpose artificial intelligence hardware accelerators. Here, we experimentally demonstrate a silicon photonic chip that can perform training and testing of a Hopfield network, i.e. recurrent neural network, via vector dot products. We demonstrate that after online training, our trained Hopfield network can successfully reconstruct corrupted input patterns.

Original languageEnglish (US)
Article number024006
JournalJPhys Photonics
Volume3
Issue number2
DOIs
StatePublished - Apr 2021

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

Keywords

  • Artificial intelligence hardware
  • Brain-inspired computing
  • Neuromorphic photonics
  • Photonic integrated circuits
  • Recurrent neural network

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