Nonlinear All-Optical Diffractive Deep Neural Network with 10.6 μm Wavelength for Image Classification

Abstract

A photonic artificial intelligence chip is based on an optical neural network (ONN), low power consumption, low delay, and strong antiinterference ability. The all-optical diffractive deep neural network has recently demonstrated its inference capabilities on the image classification task. However, the size of the physical model does not have miniaturization and integration, and the optical nonlinearity is not incorporated into the diffraction neural network. By introducing the nonlinear characteristics of the network, complex tasks can be completed with high accuracy. In this study, a nonlinear all-optical diffraction deep neural network (N-D2NN) model based on 10.6 μm wavelength is constructed by combining the ONN and complex-valued neural networks with the nonlinear activation function introduced into the structure. To be specific, the improved activation function of the rectified linear unit (ReLU), i.e., Leaky-ReLU, parametric ReLU (PReLU), and randomized ReLU (RReLU), is selected as the activation function of the N-D2NN model. Through numerical simulation, it is proved that the N-D2NN model based on 10.6 μm wavelength has excellent representation ability, which enables them to perform classification learning tasks of the MNIST handwritten digital dataset and Fashion-MNIST dataset well, respectively. The results show that the N-D2NN model with the RReLU activation function has the highest classification accuracy of 97.86% and 89.28%, respectively. These results provide a theoretical basis for the preparation of miniaturized and integrated N-D2NN model photonic artificial intelligence chips.

Application and Research of the Image Segmentation Algorithm in Remote Sensing Image Buildings

“…In the improved edge correction module, four holes convolutions with expansion rates of 1, 6, 12, and 18 are used to extract image features, and then the extracted feature maps are superimposed. After each convolution operation, normalization, and in the activation operation, in order to avoid the phenomenon of neuron necrosis in ReLU [23][24][25][26][27][28], GELU is also selected as the activation function, and then the standard convolution of 3 × 3 is used to convert the number of feature map channels to 1, and then the obtained feature map is compared with the input image of this module. Fusion is performed to obtain the preliminary information of the prediction module, and finally, the fused feature map is classified by the Sigmoid function to obtain the final segmentation result map [29][30][31][32][33].…”

High accuracy inference by an optical neural network implementation

“…Lately, a new processor based on an optical NN has attracted attention because it can process information with less energy than electric ones [7][8][9] . Optical NNs perform matrix calculations by propagation and diffraction of light.…”