A Comprehensive Survey on Nanophotonics Neural Networks

TL;DR

This survey reviews recent advances in nanophotonic neuromorphic circuits, highlighting their design, training, and optimization methods, and emphasizing their potential to revolutionize high-speed, energy-efficient intelligent systems.

Contribution

It provides a comprehensive overview of nanophotonic neural network development, including architectural innovations, training techniques, and unique optical activation functions.

Findings

Nanophotonic circuits enable faster data processing.

Innovative optical activation functions improve neural network performance.

Nanophotonic neuromorphic processors show promise for energy-efficient AI.

Abstract

In the last years, materializations of neuromorphic circuits based on nanophotonic arrangements have been proposed, which contain complete optical circuits, laser, photodetectors, photonic crystals, optical fibers, flat waveguides, and other passive optical elements of nanostructured materials, which eliminate the time of simultaneous processing of big groups of data, taking advantage of the quantum perspective and thus highly increasing the potentials of contemporary intelligent computational systems. This article is an effort to record and study the research that has been conducted concerning the methods of development and materi-alization of neuromorphic circuits of Neural Networks of nanophotonic arrangements. In particular, an investigative study of the methods of developing nanophotonic neuromorphic processors, their originality in neuronic architectural structure, their training methods and their optimization has been realized along with the study of special issues such as optical activation functions and cost functions.