Handwritten digit recognition by spin waves in a Skyrmion reservoir

被引:0
|
作者
Mu-Kun Lee
Masahito Mochizuki
机构
[1] Waseda University,Department of Applied Physics
来源
关键词
D O I
暂无
中图分类号
学科分类号
摘要
By performing numerical simulations for the handwritten digit recognition task, we demonstrate that a magnetic skyrmion lattice confined in a thin-plate magnet possesses high capability of reservoir computing. We obtain a high recognition rate of more than 88%, higher by about 10% than a baseline taken as the echo state network model. We find that this excellent performance arises from enhanced nonlinearity in the transformation which maps the input data onto an information space with higher dimensions, carried by interferences of spin waves in the skyrmion lattice. Because the skyrmions require only application of static magnetic field instead of nanofabrication for their creation in contrast to other spintronics reservoirs, our result consolidates the high potential of skyrmions for application to reservoir computing devices.
引用
收藏
相关论文
共 50 条
  • [21] Using generative models for handwritten digit recognition
    Revow, M
    Williams, CKI
    Hinton, GE
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1996, 18 (06) : 592 - 606
  • [22] A trainable feature extractor for handwritten digit recognition
    Lauer, Fabien
    Suen, Ching Y.
    Bloch, Gerard
    PATTERN RECOGNITION, 2007, 40 (06) : 1816 - 1824
  • [23] Handwritten Digit Recognition Using Bayesian ResNet
    Mhasakar P.
    Trivedi P.
    Mandal S.
    Mitra S.K.
    SN Computer Science, 2021, 2 (5)
  • [24] Neocognitron of a new version: Handwritten digit recognition
    Fukushima, K
    ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS, 2001, 2130 : 987 - 992
  • [25] A Convolutional Neural Network for Handwritten Digit Recognition
    Guevara Neri, Maria Cristina
    Vergara Villegas, Osslan Osiris
    Cruz Sanchez, Vianey Guadalupe
    Nandayapa, Manuel
    Sossa Azuela, Juan Humberto
    INTERNATIONAL JOURNAL OF COMBINATORIAL OPTIMIZATION PROBLEMS AND INFORMATICS, 2020, 11 (01): : 97 - 105
  • [26] Metaheuristics for Feature Selection in Handwritten Digit Recognition
    Seijas, Leticia M.
    Carneiro, Raphael F.
    Santana, Clodomir J., Jr.
    Soares, Larissa S. L.
    Bezerra, Sabrina G. T. A.
    Bastos-Filho, Carmelo J. A.
    2015 LATIN AMERICA CONGRESS ON COMPUTATIONAL INTELLIGENCE (LA-CCI), 2015,
  • [27] FPGA Implementation of CNN for Handwritten Digit Recognition
    Xiao, Rui
    Shi, Junsheng
    Zhang, Chao
    PROCEEDINGS OF 2020 IEEE 4TH INFORMATION TECHNOLOGY, NETWORKING, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (ITNEC 2020), 2020, : 1128 - 1133
  • [28] Handwritten digit recognition: A neural network demo
    van der Zwaag, BJ
    COMPUTATIONAL INTELLIGENCE: THEORY AND APPLICATIONS, PROCEEDINGS, 2001, 2206 : 762 - 771
  • [29] Hypergeometric Laguerre Moment for Handwritten Digit Recognition
    Benzoubeir, S.
    Hmamed, A.
    Qjidaa, H.
    2009 INTERNATIONAL CONFERENCE ON MULTIMEDIA COMPUTING AND SYSTEMS (ICMCS 2009), 2009, : 448 - 452
  • [30] Handwritten digit recognition with fuzzy neural networks
    Zhao, Hongyu
    Ye, Wenxia
    Jin, Fan
    Xinan Jiaotong Daxue Xuebao/Journal of Southwest Jiaotong University, 1997, 32 (03): : 247 - 252