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A Comprehensive Survey on Nanophotonic Neural Networks: Architectures, Training Methods, Optimization, and

Konstantinos Demertzis1,2, Georgios D Papadopoulos1, Lazaros Iliadis3

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This study reviews nanophotonic neuromorphic circuits, detailing their development, architectures, and training. It highlights their potential for advanced intelligent computing by processing data faster using optical elements.

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Area of Science:

  • Optoelectronics and Photonics
  • Neuromorphic Engineering
  • Computational Science

Background:

  • Emergence of nanophotonic circuits for advanced computational systems.
  • Integration of optical components like lasers, photodetectors, and waveguides in nanostructured materials.
  • Potential for quantum-enhanced, simultaneous data processing to boost intelligent systems.

Purpose of the Study:

  • To compile and analyze research on the development and realization of nanophotonic neuromorphic circuits.
  • To investigate methods for developing nanophotonic neuromorphic processors, including architectural design, training, and optimization.
  • To examine specific challenges such as optical activation and cost functions.

Main Methods:

  • Comprehensive literature review and meta-analysis of existing research.
  • Detailed study of nanophotonic neuromorphic processor development methodologies.
  • Analysis of architectural structures, training algorithms, optimization techniques, and optical function implementations.

Main Results:

  • First comprehensive presentation of known architectures, training methods, optimization strategies, and activation functions for nanophotonic networks.
  • Identification and analysis of the advantages and disadvantages of nanophotonic networks.
  • Demonstration of the potential for significant increases in computational power and processing speed.

Conclusions:

  • Nanophotonic neuromorphic circuits offer a promising avenue for next-generation intelligent computing systems.
  • This work provides a foundational overview for researchers in the field.
  • Further research into optical activation functions and cost functions is crucial for optimization.