MNIST数据库
计算机科学
规范化(社会学)
卷积神经网络
人工神经网络
卷积(计算机科学)
谐振器
电子工程
光学
算法
材料科学
人工智能
光电子学
物理
工程类
社会学
人类学
作者
Jingya Ding,Lianqing Zhu,Mingxin Yu,Lidan Lu,Penghao Hu
出处
期刊:Optics Express
[Optica Publishing Group]
日期:2024-02-05
卷期号:32 (5): 7832-7832
被引量:1
摘要
We propose an improved optical neural network (ONN) circuit architecture based on conventional micro-resonator ONNs, called the Phase-based Micro-resonator Optical Neural Network (PMONN). PMONN's core architecture features a Convolutions and Batch Normalization (CB) unit, comprising a phase-based (PB) convolutional layer, a Depth-Point-Wise (DPW) convolutional layer, and a reconstructed Batch Normalization (RBN) layer. The PB convolution kernel uses modulable phase shifts of Add-drop MRRs as learnable parameters and their optical transfer function as convolution weights. The DPW convolution kernel amplifies PB convolution weights by learning the amplification factors. To address the internal covariate shift during training, the RBN layer normalizes DPW outputs by reconstructing the BN layer of the electronic neural network, which is then merged with the DPW layer in the test stage. We employ the tunable DAs in the architecture to implement the merged layer. PMONN achieves 99.15% and 91.83% accuracy on MNIST and Fashion-MNIST datasets, respectively. This work presents a method for implementing an optical neural network on the improved architecture based on MRRs and increases the flexibility and reusability of the architecture. PMONN has potential applications as the backbone for future optical object detection neural networks.
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