Numerical simulations on optoelectronic deep neural network hardware based on self-referential holography
Numerical simulations on optoelectronic deep neural network hardware based on self-referential holography
复制标题
基于自参考全息的光电深度神经网络硬件数值模拟
DOI：
10.1007/s10043-023-00810-2
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发表时间：
2023
期刊：
影响因子：
1.2
通讯作者：
M. Takabayashi
中科院分区：
文献类型：
--
作者：
Rio Tomioka;M. Takabayashi
我们提出了一种新型光电深度神经网络（OE-DNN）硬件，称为自参考全息深度神经网络（SR-HDNN）。 SR-HDNN 结合了利用体积全息图的光学计算部分和虚拟连接光学元件的电子部分。由于体积全息图的形状（在这种情况下是3维（3D）折射率分布）可以通过其记录条件来改变，因此有望通过特定节点之间的耦合来实现光学计算功能的灵活设计。此外，电子部分无需扩展光学系统即可构建多层网络，并可实现包括非线性运算在内的任意信号处理。通过集成柔性光学和电子部件，由柔性光学和电子部件组成的SR-HDNN有潜力最大化OE-DNN的性能。在本研究中，我们对图像分类任务进行数值模拟，以研究 SR-HDNN 的可行性和潜力。
We propose a novel optoelectronic deep neural network (OE-DNN) hardware called the self-referential holographic deep neural network (SR-HDNN). The SR-HDNN features a combination of an optical computing part utilizing a volume hologram and an electronic part connecting the optical elements virtually. Since the shape of a volume hologram, which is a 3-dimensional (3D) refractive index distribution in this case, can be changed by its recording conditions, it is expected to realize the flexible design of optical computing functions by coupling between specific nodes. In addition, the electronic part enables the construction of multi-layer networks without extending the optical system and enabling arbitrary signal processing, including nonlinear operations. By integrating flexible optical and electronic parts, the SR-HDNN consisting of both flexible optical and electronic parts has the potential to maximize the performance of OE-DNN. In this study, we numerically simulate image classification tasks to investigate the feasibility and potential of the SR-HDNN.
影响因子：
3.8
作者：
M. Takabayashi;A. Okamoto
通讯作者：
M. Takabayashi;A. Okamoto