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基于自由空间光学、使用透镜阵列和空间光调制器的可扩展光学卷积神经网络。

Scalable Optical Convolutional Neural Networks Based on Free-Space Optics Using Lens Arrays and a Spatial Light Modulator.

作者信息

Ju Young-Gu

机构信息

Department of Physics Education, Kyungpook National University, 80 Daehakro, Bukgu, Daegu 41566, Republic of Korea.

出版信息

J Imaging. 2023 Nov 6;9(11):241. doi: 10.3390/jimaging9110241.

Abstract

A scalable optical convolutional neural network (SOCNN) based on free-space optics and Koehler illumination was proposed to address the limitations of the previous 4f correlator system. Unlike Abbe illumination, Koehler illumination provides more uniform illumination and reduces crosstalk. The SOCNN allows for scaling of the input array and the use of incoherent light sources. Hence, the problems associated with 4f correlator systems can be avoided. We analyzed the limitations in scaling the kernel size and parallel throughput and found that the SOCNN can offer a multilayer convolutional neural network with massive optical parallelism.

摘要

提出了一种基于自由空间光学和柯勒照明的可扩展光学卷积神经网络(SOCNN),以解决先前4f相关器系统的局限性。与阿贝照明不同,柯勒照明提供更均匀的照明并减少串扰。SOCNN允许输入阵列的扩展以及非相干光源的使用。因此,可以避免与4f相关器系统相关的问题。我们分析了内核大小扩展和平行吞吐量方面的局限性,发现SOCNN可以提供具有大规模光学并行性的多层卷积神经网络。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/38d9/10672105/534a01e4cbff/jimaging-09-00241-g001.jpg

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