Optical neuromorphic computing via temporal up-sampling and trainable encoding on a telecom device platform

IF 6.6 2区 物理与天体物理 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Egor Manuylovich, Dmitrii Stoliarov, David Saad, Sergei K. Turitsyn
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Abstract

Mapping input signals to a high-dimensional space is a critical concept in various neuromorphic computing paradigms, including models such as reservoir computing (RC) and extreme learning machines (ELM). We propose using commercially available telecom devices and technologies developed for high-speed optical data transmission to implement these models through nonlinear mapping of optical signals into a high-dimensional space where linear processing can be applied. We manipulate the output feature dimension by applying temporal up-sampling (at the speed of commercially available telecom devices) of input signals and a well-established wave-division-multiplexing (WDM). Our up-sampling approach utilizes a trainable encoding mask, where each input symbol is replaced with a structured sequence of masked symbols, effectively increasing the representational capacity of the feature space. This gives remarkable flexibility in the dynamical phase masking of the input signal. We demonstrate this approach in the context of RC and ELM, employing readily available photonic devices, including a semiconductor optical amplifier and nonlinear Mach–Zehnder interferometer (MZI). We investigate how nonlinear mapping provided by these devices can be characterized in terms of the increased controlled separability and predictability of the output state.
电信设备平台上基于时间上采样和可训练编码的光学神经形态计算
将输入信号映射到高维空间是各种神经形态计算范式中的一个关键概念,包括水库计算(RC)和极限学习机(ELM)等模型。我们建议使用商业上可用的电信设备和为高速光数据传输开发的技术,通过将光信号非线性映射到可以应用线性处理的高维空间来实现这些模型。我们通过应用输入信号的时间上采样(以商用电信设备的速度)和完善的波分复用(WDM)来操纵输出特征维度。我们的上采样方法利用了一个可训练的编码掩码,其中每个输入符号都被一个结构化的掩码符号序列所取代,有效地增加了特征空间的表示能力。这为输入信号的动态相位掩蔽提供了显著的灵活性。我们在RC和ELM的背景下演示了这种方法,使用现成的光子器件,包括半导体光放大器和非线性马赫-曾德尔干涉仪(MZI)。我们研究了这些器件所提供的非线性映射是如何以输出状态的可控可分性和可预测性的增加来表征的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nanophotonics
Nanophotonics NANOSCIENCE & NANOTECHNOLOGY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
13.50
自引率
6.70%
发文量
358
审稿时长
7 weeks
期刊介绍: Nanophotonics, published in collaboration with Sciencewise, is a prestigious journal that showcases recent international research results, notable advancements in the field, and innovative applications. It is regarded as one of the leading publications in the realm of nanophotonics and encompasses a range of article types including research articles, selectively invited reviews, letters, and perspectives. The journal specifically delves into the study of photon interaction with nano-structures, such as carbon nano-tubes, nano metal particles, nano crystals, semiconductor nano dots, photonic crystals, tissue, and DNA. It offers comprehensive coverage of the most up-to-date discoveries, making it an essential resource for physicists, engineers, and material scientists.
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