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
  • [1] Handwritten digit recognition by spin waves in a Skyrmion reservoir
    Lee, Mu-Kun
    Mochizuki, Masahito
    SCIENTIFIC REPORTS, 2023, 13 (01)
  • [2] Reservoir Computing with Spin Waves in a Skyrmion Crystal
    Lee, Mu-Kun
    Mochizuki, Masahito
    PHYSICAL REVIEW APPLIED, 2022, 18 (01)
  • [3] Arabic handwritten digit recognition
    Sherif Abdleazeem
    Ezzat El-Sherif
    International Journal of Document Analysis and Recognition (IJDAR), 2008, 11 : 127 - 141
  • [4] Arabic handwritten digit recognition
    Abdleazeem, Sherif
    El-Sherif, Ezzat
    INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2008, 11 (03) : 127 - 141
  • [5] FIRMLP for Handwritten Digit Recognition
    Codrescu, Cristinel
    PROCEEDINGS OF 2016 15TH INTERNATIONAL CONFERENCE ON FRONTIERS IN HANDWRITING RECOGNITION (ICFHR), 2016, : 483 - 488
  • [6] Neocognitron for handwritten digit recognition
    Fukushima, K
    NEUROCOMPUTING, 2003, 51 : 161 - 180
  • [7] Comparison of Feature Extraction Techniques for Handwritten Digit Recognition with a Photonic Reservoir Computer
    Antonik, Piotr
    Marsal, Nicolas
    Brunner, Daniel
    Rontani, Damien
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: WORKSHOP AND SPECIAL SESSIONS, 2019, 11731 : 175 - 179
  • [8] Handwritten English Character and Digit Recognition
    Al-Mahmud
    Tanvin, Asnuva
    Rahman, Sazia
    PROCEEDINGS OF INTERNATIONAL CONFERENCE ON ELECTRONICS, COMMUNICATIONS AND INFORMATION TECHNOLOGY 2021 (ICECIT 2021), 2021,
  • [9] Rosenblatt Perceptrons for handwritten digit recognition
    Ernst, K
    Tatyana, B
    Lora, K
    Vladimir, L
    IJCNN'01: INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS, 2001, : 1516 - 1520
  • [10] Ranked Dropout for Handwritten Digit Recognition
    Tang, Yue
    Liang, Zhuonan
    Shi, Huaze
    Fu, Peng
    Sun, Quansen
    TWELFTH INTERNATIONAL CONFERENCE ON GRAPHICS AND IMAGE PROCESSING (ICGIP 2020), 2021, 11720