Photonic reservoir computing for parallel task processing based on a feedback-free spin-polarized VCSEL

IF 2.2 3区 物理与天体物理 Q2 OPTICS
Yigong Yang, Yu Huang, Pei Zhou, Nianqiang Li
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引用次数: 0

Abstract

Time delayed reservoir computing (RC) is a novel artificial neural network that is easy to implement in hardware due to its extremely simple structure. Because of its time-division multiplexed information processing, laser-based photonic time-delayed RCs usually realize parallel processing with polarization/wavelength multiplexing. However, the performance of two different tasks is difficult to regulate separately and simultaneously in the time delayed RC system, especially for the chip-scale configuration. Here, we propose a feedback-free RC system based on a spin-polarized vertical-cavity surface-emitting semiconductor laser (VCSEL), which simplifies the whole system structure and can process time series prediction and waveform recognition tasks in parallel, with employing the input and output coding to provide the effect from past states. By separately setting the number of past states introduced by the coding for the two tasks, the performance of the two tasks can be adjusted respectively. Furthermore, by appropriately tuning the pump polarization ellipticity which is the unique feature for the spin-polarized VCSEL, the computational ability of the proposed RC can be focused on one of the two parallel tasks. Therefore, the proposed RC system is capable of dealing with different tasks with high performance, and also expected to provide a viable solution for integrated neuromorphic computing systems due to its compact, feedback-free structure.
基于无反馈自旋极化 VCSEL 的用于并行任务处理的光子存储计算
延时存储计算(RC)是一种新型人工神经网络,由于其结构极其简单,因此很容易在硬件中实现。由于具有时分复用信息处理功能,基于激光的光子延时 RC 通常可以实现偏振/波长复用的并行处理。然而,在延时 RC 系统中,很难同时单独调节两种不同任务的性能,尤其是在芯片级配置中。在此,我们提出了一种基于自旋偏振垂直腔表面发射半导体激光器(VCSEL)的无反馈 RC 系统,它简化了整个系统的结构,可以并行处理时间序列预测和波形识别任务,并利用输入和输出编码提供过去状态的效果。通过分别设置两个任务的编码所引入的过去状态的数量,可以分别调整两个任务的性能。此外,通过适当调整泵浦偏振椭圆度(这是自旋偏振 VCSEL 的独有特征),可以将拟议 RC 的计算能力集中于两个并行任务中的一个。因此,所提出的 RC 系统能够高性能地处理不同的任务,而且由于其结构紧凑、无反馈,有望为集成神经形态计算系统提供可行的解决方案。
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来源期刊
Optics Communications
Optics Communications 物理-光学
CiteScore
5.10
自引率
8.30%
发文量
681
审稿时长
38 days
期刊介绍: Optics Communications invites original and timely contributions containing new results in various fields of optics and photonics. The journal considers theoretical and experimental research in areas ranging from the fundamental properties of light to technological applications. Topics covered include classical and quantum optics, optical physics and light-matter interactions, lasers, imaging, guided-wave optics and optical information processing. Manuscripts should offer clear evidence of novelty and significance. Papers concentrating on mathematical and computational issues, with limited connection to optics, are not suitable for publication in the Journal. Similarly, small technical advances, or papers concerned only with engineering applications or issues of materials science fall outside the journal scope.
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