Machine Learning with Neuromorphic Photonics

Thomas Ferreira De Lima, Hsuan Tung Peng, Alexander N. Tait, Mitchell A. Nahmias, Heidi B. Miller, Bhavin J. Shastri, Paul R. Prucnal

Research output: Contribution to journalArticlepeer-review

137 Scopus citations


Neuromorphic photonics has experienced a recent surge of interest over the last few years, promising orders of magnitude improvements in both speed and energy efficiency over digital electronics. This paper provides a tutorial overview of neuromorphic photonic systems and their application to optimization and machine learning problems. We discuss the physical advantages of photonic processing systems, and we describe underlying device models that allow practical systems to be constructed. We also describe several real-world applications for control and deep learning inference. Finally, we discuss scalability in the context of designing a full-scale neuromorphic photonic processing system, considering aspects such as signal integrity, noise, and hardware fabrication platforms. The paper is intended for a wide audience and teaches how theory, research, and device concepts from neuromorphic photonics could be applied in practical machine learning systems.

Original languageEnglish (US)
Article number8662590
Pages (from-to)1515-1534
Number of pages20
JournalJournal of Lightwave Technology
Issue number5
StatePublished - Mar 1 2019

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics


  • Deep learning
  • machine learning
  • more-than-Moore computing
  • neuromorphic photonics
  • nonlinear programming
  • optimization
  • photonic hardware accelerator
  • photonic integrated circuits
  • photonic neural networks
  • silicon photonics
  • wavelength-division multiplexing (WDM)


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