As the demand for computational performance in artificial intelligence (AI) continues to increase, diffractive deep neural networks (DNNs), which can perform AI computing at the speed of light by repeated optical modulation with diffractive optical elements (DOEs), are attracting attention. DOEs are ...
而在衍射神经网络中,这两个数据(权重和偏置)与的两个部分紧密相关:光相位和幅值的改变以及衍射层的透射和反射系数。光相位和幅度的改变是由瑞利-索末菲衍射积分公式所确定的,而偏置项是由层的透射系数所确定的。衍射神经网络模型如下图,下面来详细介绍一下结构。 关于神经元之间连接的权重,通过瑞利-索末菲衍射积...
Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning ...
www.nature.com/scientificreports OPEN Wavefront-aberration-tolerant diffractive deep neural networks using volume holographic optical elements Ikuo Hoshi, Koki Wakunami,Yasuyuki Ichihashi & Ryutaro Oi As the demand for computational performance in artificial intelligence (AI) continues to ...
在深度学习领域,Kaiming He大佬的残差神经网络resnet可谓无人不知,那么,这一结构,能否运用于衍射神经网络呢?这就是这次的论文所提出的想法。 论文原文地址: https://opg.optica.org/ol/abstract.cfm?uri=ol-45-10-2688 opg.optica.org/ol/abstract.cfm?uri=ol-45-10-2688 ...
Diffraction Deep Neural Networks(D2NN). Contribute to chuqianyu/Diffractive-Deep-Neural-Networks development by creating an account on GitHub.
(2018)All-optical machine learning using diffractive deep neural networks,程序员大本营,技术文章内容聚合第一站。
As an analogy to standard deep neural networks (see Fig. 1D), one can consider the transmission/reflection coefficient of each point/neuron as a multiplicative “bias” term, which is a learnable network parameter that is iteratively adjusted during the training process of the diffractive ...
Diffractive deep neural networks (D2NNs) typically adopt a densely cascaded arrangement of diffractive masks, leading to multiple reflections of diffracted light between adjacent masks, thereby affecting the network's inference capability. It is challenging to fully simulate this multiple-reflection phenome...
We introduce an all-optical Diffractive Deep Neural Network (D2NN) architecture that can learn to implement various functions after deep learning-based design of passive diffractive layers that work collectively. We experimentally demonstrated the success of this framework by creating 3D-printed D2NNs...