Reservoir computing based on a silicon microring and time multiplexing for binary and analog operations

Author:

Borghi Massimo,Biasi Stefano,Pavesi Lorenzo

Abstract

AbstractPhotonic implementations of reservoir computing (RC) promise to reach ultra-high bandwidth of operation with moderate training efforts. Several optoelectronic demonstrations reported state of the art performances for hard tasks as speech recognition, object classification and time series prediction. Scaling these systems in space and time faces challenges in control complexity, size and power demand, which can be relieved by integrated optical solutions. Silicon photonics can be the disruptive technology to achieve this goal. However, the experimental demonstrations have been so far focused on spatially distributed reservoirs, where the massive use of splitters/combiners and the interconnection loss limits the number of nodes. Here, we propose and validate an all optical RC scheme based on a silicon microring (MR) and time multiplexing. The input layer is encoded in the intensity of a pump beam, which is nonlinearly transferred to the free carrier concentration in the MR and imprinted on a secondary probe. We harness the free carrier dynamics to create a chain-like reservoir topology with 50 virtual nodes. We give proof of concept demonstrations of RC by solving two nontrivial tasks: the delayed XOR and the classification of Iris flowers. This forms the basic building block from which larger hybrid spatio-temporal reservoirs with thousands of nodes can be realized with a limited set of resources.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Reference48 articles.

1. Russakovsky, O. et al. Imagenet large scale visual recognition challenge. Int. J. Comput. Vis 115, 211–252 (2015).

2. Buetti-Dinh, A. et al. Deep neural networks outperform human experts capacity in characterizing bioleaching bacterial biofilm composition. Biotechnol. Rep. 22, e00321 (2019).

3. Silver, D. et al. Mastering the game of go with deep neural networks and tree search. Nature 529, 484–489 (2016).

4. Assael, Y. M., Shillingford, B., Whiteson, S. & De Freitas, N. Lipnet: End-to-end sentence-level lipreading. arXiv preprint: arXiv:1611.01599 (2016).

5. Jaeger, H. The echo state approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German National Research Center for Information Technology GMD Technical Report 148, 13 (2001).

Cited by 30 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Self-pulsation and synchronization of optical neurons based on microrings;Optics & Laser Technology;2024-05

2. Reducing reservoir computer hyperparameter dependence by external timescale tailoring;Neuromorphic Computing and Engineering;2024-01-22

3. Effects of cavity nonlinearities and linear losses on silicon microring-based reservoir computing;Optics Express;2024-01-05

4. Photonic Neural Networks Based on Integrated Silicon Microresonators;Intelligent Computing;2024-01

5. Neuromorphic Computing with the Plasmonic Microcavity for all Types of Logic Tasks;2023 Asia Communications and Photonics Conference/2023 International Photonics and Optoelectronics Meetings (ACP/POEM);2023-11-04

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3