Communications Physics (Mar 2024)

Fiber optic computing using distributed feedback

  • Brandon Redding,
  • Joseph B. Murray,
  • Joseph D. Hart,
  • Zheyuan Zhu,
  • Shuo S. Pang,
  • Raktim Sarma

DOI
https://doi.org/10.1038/s42005-024-01549-1
Journal volume & issue
Vol. 7, no. 1
pp. 1 – 10

Abstract

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Abstract The widespread adoption of machine learning and other matrix intensive computing algorithms has renewed interest in analog optical computing, which has the potential to perform large-scale matrix multiplications with superior energy scaling and lower latency than digital electronics. However, most optical techniques rely on spatial multiplexing, requiring a large number of modulators and detectors, and are typically restricted to performing a single kernel convolution operation per layer. Here, we introduce a fiber-optic computing architecture based on temporal multiplexing and distributed feedback that performs multiple convolutions on the input data in a single layer. Using Rayleigh backscattering in standard single mode fiber, we show that this technique can efficiently apply a series of random nonlinear projections to the input data, facilitating a variety of computing tasks. The approach enables efficient energy scaling with orders of magnitude lower power consumption than GPUs, while maintaining low latency and high data-throughput.