光子集成电路全光机器学习飞跃综述

IF 1 Q4 OPTICS
Ankur Saharia, Kamalkishor Choure, Nitesh Mudgal, Ravi Kumar Maddila, Manish Tiwari, Ghanshyam Singh
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引用次数: 0

摘要

人脑是地球上最复杂的电路,而受生物神经元运作启发的电路是最理想的计算需求。人工神经网络(ANN)是一种可以复制生物神经元的电路。光计算已经在集成电路技术中创造了奇迹,因此神经网络的光子实现由于其低功耗和高带宽而成为当前时代最具吸引力的技术之一。人工神经网络模型是根据人类大脑的信号处理设计的,因此它们可以用来提高任何系统的分析能力。本文综述了光神经网络的研究进展及其应用前景。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Introductory Review on All-Optical Machine Learning Leap in Photonic Integrated Circuits

Introductory Review on All-Optical Machine Learning Leap in Photonic Integrated Circuits

The human brain is the most complex circuit on the planet and the circuits inspired by the operation of the biological neuron are the most desired computing need. Artificial neural networks (ANN) are circuits that can replicate the biological neuron. Optical computing already doing wonders in integrated circuit technology and therefore the photonic implementation of neural networks is one of the most appealing technologies of the current era due to its low power consumption and high bandwidth. The ANN models are designed as per the signal processing of the human brain therefore they can be used to improve the analytic power of any system. This article reviews the advancement in optical neural networks and their application for future perspective.

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来源期刊
CiteScore
1.50
自引率
11.10%
发文量
25
期刊介绍: The journal covers a wide range of issues in information optics such as optical memory, mechanisms for optical data recording and processing, photosensitive materials, optical, optoelectronic and holographic nanostructures, and many other related topics. Papers on memory systems using holographic and biological structures and concepts of brain operation are also included. The journal pays particular attention to research in the field of neural net systems that may lead to a new generation of computional technologies by endowing them with intelligence.
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