基于层间全模拟连接频率复用的深层光子储层计算机

IF 8.4 1区 物理与天体物理 Q1 OPTICS
Optica Pub Date : 2023-11-06 DOI:10.1364/optica.489501
Alessandro Lupo, Enrico Picco, Marina Zajnulina, and Serge Massar
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引用次数: 0

摘要

油藏计算机(RC)是一种随机递归神经网络,非常适合处理时间序列,执行非线性失真补偿或混沌动力学预测等任务。深层储层计算机(Deep RC)将一个储层的输出用作另一个储集层的输入,可以提高性能,因为与其他深层人工神经网络一样,连续层以越来越抽象的方式表示数据。我们提出了一种基于光纤的基于频率复用的两层深RC的光子实现。两个RC层被编码在在相同实验设置中传播的两个频率梳中。层之间的连接是完全模拟的,不需要任何数字处理。我们发现,深度RC在两个基准任务上比传统RC高出两个数量级。这项工作为使用全模拟光子神经形态计算进行时间序列的复杂处理铺平了道路,同时避免了昂贵的模数和数模转换。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep photonic reservoir computer based on frequency multiplexing with fully analog connection between layers
Reservoir computers (RCs) are randomized recurrent neural networks well adapted to process time series, performing tasks such as nonlinear distortion compensation or prediction of chaotic dynamics. Deep reservoir computers (deep-RCs), in which the output of one reservoir is used as the input for another one, can lead to improved performance because, as in other deep artificial neural networks, the successive layers represent the data in more and more abstract ways. We present a fiber-based photonic implementation of a two-layer deep-RC based on frequency multiplexing. The two RC layers are encoded in two frequency combs propagating in the same experimental setup. The connection between the layers is fully analog and does not require any digital processing. We find that the deep-RC outperforms a traditional RC by up to two orders of magnitude on two benchmark tasks. This work paves the way towards using fully analog photonic neuromorphic computing for complex processing of time series, while avoiding costly analog-to-digital and digital-to-analog conversions.
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来源期刊
Optica
Optica OPTICS-
CiteScore
19.70
自引率
2.90%
发文量
191
审稿时长
2 months
期刊介绍: Optica is an open access, online-only journal published monthly by Optica Publishing Group. It is dedicated to the rapid dissemination of high-impact peer-reviewed research in the field of optics and photonics. The journal provides a forum for theoretical or experimental, fundamental or applied research to be swiftly accessed by the international community. Optica is abstracted and indexed in Chemical Abstracts Service, Current Contents/Physical, Chemical & Earth Sciences, and Science Citation Index Expanded.
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