人工神经网络
计算机科学
非线性系统
计算
构造(python库)
激活函数
透明度(行为)
伊辛模型
电子工程
拓扑(电路)
人工智能
算法
物理
工程类
统计物理学
电气工程
量子力学
程序设计语言
计算机安全
作者
Ying Zuo,Bohan Li,Yujun Zhao,Yue Jiang,You-Chiuan Chen,Peng Chen,Gyu-Boong Jo,Junwei Liu,Shengwang Du
出处
期刊:Optica
[Optica Publishing Group]
日期:2019-08-29
卷期号:6 (9): 1132-1132
被引量:97
标识
DOI:10.1364/optica.6.001132
摘要
Artificial neural networks (ANNs) have now been widely used for industry applications and also played more important roles in fundamental researches. Although most ANN hardware systems are electronically based, optical implementation is particularly attractive because of its intrinsic parallelism and low energy consumption. Here, we propose and demonstrate fully-functioned all optical neural networks (AONNs), in which linear operations are programmed by spatial light modulators and Fourier lenses, and optical nonlinear activation functions are realized with electromagnetically induced transparency in laser-cooled atoms. Moreover, all the errors from different optical neurons here are independent, thus the AONN could scale up to a larger system size with final error still maintaining in a similar level of a single neuron. We confirm its capability and feasibility in machine learning by successfully classifying the order and disorder phases of a typical statistic Ising model. The demonstrated AONN scheme can be used to construct various ANNs of different architectures with the intrinsic parallel computation at the speed of light.
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