Experimental demonstration of reservoir computing with a silicon resonator and time multiplexing

M. Borghi, S. Biasi, Lorenzo Pavesi
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Abstract

Reservoir computing (RC) replaces the backbone of deep neural networks with the dynamics of a complex physical system in which only the output synapses are trained. Optical phenomena form a natural substrate for these architectures, while integrated optics can be used to enhance the nonlinear effects. Here, we propose and experimentally validate an all optical RC scheme based on a silicon on insulator microresonator (MR) and time multiplexing. We give proof of concept demonstrations of RC by solving two nontrivial tasks: the delayed XOR and the classification of the Iris flowers dataset. The approach could be scaled up to realize large hybrid spatio-temporal reservoirs of increased computational speed and complexity.
用硅谐振器和时间复用进行储层计算的实验演示
水库计算(RC)取代了深层神经网络的骨干与一个复杂的物理系统的动态,其中只有输出突触被训练。光学现象为这些结构提供了自然的基础,而集成光学可以用来增强非线性效应。在此,我们提出并实验验证了一种基于绝缘体硅微谐振器(MR)和时间复用的全光RC方案。我们通过解决两个重要的任务:延迟异或和鸢尾花数据集的分类,给出了RC的概念证明。该方法可以扩展到实现计算速度和复杂度更高的大型混合时空存储库。
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