光子学
MNIST数据库
人工神经网络
计算机科学
非线性系统
实现(概率)
电子工程
光子集成电路
物理
光电子学
工程类
人工智能
数学
量子力学
统计
作者
Zefeng Xu,Baoshan Tang,Xiangyu Zhang,Jin Feng Leong,Jieming Pan,Sonu Hooda,Evgeny Zamburg,Aaron Thean
标识
DOI:10.1038/s41377-022-00976-5
摘要
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) MoS2 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).
科研通智能强力驱动
Strongly Powered by AbleSci AI