Scalable Networks of Neuromorphic Photonic Integrated Circuits

Lei Xu, Thomas Ferreira De Lima, Hsuan Tung Peng, Simon Bilodeau, Alexander Tait, Bhavin J. Shastri, Paul R. Prucnal

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Neuromorphic photonic integrated circuits over silicon photonic platform have recently made significant progress. Photonic neural networks with a small number of neurons have demonstrated important applications in high-bandwidth, low latency machine learning (ML) type signal processing applications. Naturally an important topic is to investigate building a large scale photonic neural networks with high flexibility and scalability to potentially support ML type applications involving high-speed processing of a high volume of data. In this paper we revisited the architecture of microring resonator (MRR)-based non-spiking and spiking photonic neurons, and photonic neural networks using broadcast-and-weight scheme. We illustrate expanded neural network topologies by cascading photonic broadcast loops, to achieve scalable neural network scalability with a fixed number of wavelengths. Furthermore, we propose the adoption of wavelength selective switch (WSS) inside the broadcasting loop for wavelength-switched photonic neural network (WS-PNN). The WS-PNN architecture will find new applications of using off-chip WSS switches to interconnect groups of photonic neurons. The interconnection of WS-PNN can achieve unprecedented scalability of photonic neural networks while supporting a versatile selection of mixture of feedforward and recurrent neural network topologies.

Original languageEnglish (US)
Article number6101409
JournalIEEE Journal of Selected Topics in Quantum Electronics
Issue number6
StatePublished - 2022

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering


  • Silicon photonic neural network
  • neuromorphic photonic computing
  • wavelength selective switching


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