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<p>Neuromorphic photonics enables brain-inspired information processing with higher bandwidth and lower energy consumption than traditional electronics, addressing the growing computational demands of the Internet of Things, cloud services, and edge computing. However, even current state-of-the-art electronic and photonic platforms are incapable of delivering the scalable throughput, multitasking processing, and energy efficiency required by these applications. Here, we demonstrate a tunable photonic reservoir computing device based on a nonlinear amplifying loop mirror (NALM), leveraging a time-delayed, single-unit, all-optical architecture. By combining dense temporal encoding with wavelength-division multiplexing, the system supports concurrent multitasking across independent data channels, enabling scalable computational performance without additional hardware complexity. Experiments and theoretical validation on classification and prediction benchmarks demonstrate the device’s performance, achieving a throughput of 20 tera-operations-per-second and an energy efficiency of 4.4 fJ per operation. These results highlight a promising path towards reconfigurable, compact, and high-performance photonic processors for real-time intelligent applications.</p>

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Scalable photonic reservoir computing for parallel machine learning tasks

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月31日

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  • A. Aadhi,
  • L. Di Lauro,
  • B. Fischer,
  • P. Dmitriev,
  • I. Alamgir,
  • C. Mazoukh,
  • N. Perron,
  • E. A. Viktorov,
  • A. V. Kovalev,
  • A. Eshaghi,
  • S. Vakili,
  • M. Chemnitz,
  • P. Roztocki,
  • B. E. Little,
  • S. T. Chu,
  • D. J. Moss,
  • R. Morandotti

摘要

Neuromorphic photonics enables brain-inspired information processing with higher bandwidth and lower energy consumption than traditional electronics, addressing the growing computational demands of the Internet of Things, cloud services, and edge computing. However, even current state-of-the-art electronic and photonic platforms are incapable of delivering the scalable throughput, multitasking processing, and energy efficiency required by these applications. Here, we demonstrate a tunable photonic reservoir computing device based on a nonlinear amplifying loop mirror (NALM), leveraging a time-delayed, single-unit, all-optical architecture. By combining dense temporal encoding with wavelength-division multiplexing, the system supports concurrent multitasking across independent data channels, enabling scalable computational performance without additional hardware complexity. Experiments and theoretical validation on classification and prediction benchmarks demonstrate the device’s performance, achieving a throughput of 20 tera-operations-per-second and an energy efficiency of 4.4 fJ per operation. These results highlight a promising path towards reconfigurable, compact, and high-performance photonic processors for real-time intelligent applications.