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基于非易失性光阻变随机存取存储器开关的用于光子神经网络的可重构非线性光子激活函数。

Reconfigurable nonlinear photonic activation function for photonic neural network based on non-volatile opto-resistive RAM switch.

作者信息

Xu Zefeng, Tang Baoshan, Zhang Xiangyu, Leong Jin Feng, Pan Jieming, Hooda Sonu, Zamburg Evgeny, Thean Aaron Voon-Yew

机构信息

Integrative Sciences and Engineering Programme, NUS Graduate School, National University of Singapore, Singapore, Singapore.

Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117583, Singapore.

出版信息

Light Sci Appl. 2022 Oct 6;11(1):288. doi: 10.1038/s41377-022-00976-5.

Abstract

Photonic neural network has been sought as an alternative solution to surpass the efficiency and speed bottlenecks of electronic neural network. Despite that the integrated Mach-Zehnder Interferometer (MZI) mesh can perform vector-matrix multiplication in photonic neural network, a programmable in-situ nonlinear activation function has not been proposed to date, suppressing further advancement of photonic neural network. Here, we demonstrate an efficient in-situ nonlinear accelerator comprising a unique solution-processed two-dimensional (2D) MoS Opto-Resistive RAM Switch (ORS), which exhibits tunable nonlinear resistance switching that allow us to introduce nonlinearity to the photonic neuron which overcomes the linear voltage-power relationship of typical photonic components. Our reconfigurable scheme enables implementation of a wide variety of nonlinear responses. Furthermore, we confirm its feasibility and capability for MNIST handwritten digit recognition, achieving a high accuracy of 91.6%. Our accelerator constitutes a major step towards the realization of in-situ photonic neural network and pave the way for the integration of photonic integrated circuits (PIC).

摘要

光子神经网络已被视作一种替代方案,以突破电子神经网络在效率和速度方面的瓶颈。尽管集成马赫曾德尔干涉仪(MZI)网格能够在光子神经网络中执行向量矩阵乘法,但迄今为止尚未提出可编程的原位非线性激活函数,这抑制了光子神经网络的进一步发展。在此,我们展示了一种高效的原位非线性加速器,它由独特的溶液处理二维(2D)MoS光阻变随机存取存储器开关(ORS)组成,该开关呈现出可调谐的非线性电阻切换特性,使我们能够将非线性引入光子神经元,从而克服典型光子组件的线性电压 - 功率关系。我们的可重构方案能够实现多种非线性响应。此外,我们证实了其在MNIST手写数字识别中的可行性和能力,实现了91.6%的高精度。我们的加速器朝着实现原位光子神经网络迈出了重要一步,为光子集成电路(PIC)的集成铺平了道路。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b167/9537414/19ff42bd6530/41377_2022_976_Fig1_HTML.jpg

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