A Photonics-Inspired Compact Network: Toward Real-Time AI Processing in Communication Systems

Hsuan Tung Peng, Joshua C. Lederman, Lei Xu, Thomas Ferreira De Lima, Chaoran Huang, Bhavin J. Shastri, David Rosenbluth, Paul R. Prucnal

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Machine learning methods are ubiquitous in communication systems and have proven powerful for applications including radio-frequency (RF) fingerprinting, automatic modulation classification, and signal recovery in communication systems. However, the high throughput requirement of a communication link makes AI models difficult to implement in real-time on edge devices. In this work, we address this issue by improving both the algorithm and hardware to target real-time AI processing in communication systems. For algorithm development, we propose the first compact deep network consisting of a silicon photonic recurrent neural network model in combination with a simplified convolutional neural network classifier to identify RF emitters by their random transmissions. Our model achieves 96.32% classification accuracy over a set of 30 identical ZigBee devices when using 50 times fewer training parameters than an existing state-of-the-art CNN classifier (Merchant et al., 2018). Thanks to the large reduction in network size, we emulate the system using a small-scale FPGA board, the PYNQ-Z1, and demonstrate real-time RF fingerprinting with 0.219 ms latency. In addition, for hardware implementation, we further demonstrate a fully-integrated silicon photonic neural network for fiber nonlinearity compensation (Huang et al., 2021), which improves the received signal by 0.60 dB.

Original languageEnglish (US)
Article number7400217
JournalIEEE Journal of Selected Topics in Quantum Electronics
Issue number4
StatePublished - 2022

All Science Journal Classification (ASJC) codes

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


  • Fiber nonlinear dispersion compensation
  • RF fingerprinting
  • Silicon photonic neural network


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