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Memristor crossbar-based unsupervised image learning

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Abstract

This letter presents a new memristor crossbar array system and demonstrates its applications in image learning. The controlled pulse and image overlay technique are introduced for the programming of memristor crossbars and promising a better performance for noise reduction. The time-slot technique is helpful for improving the processing speed of image. Simulink and numerical simulations have been employed to demonstrate the useful applications of the proposed circuit structure in image learning.

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Acknowledgments

This work was jointly supported by the Fundamental Research Funds for the Central Universities under Grant CDJXS12180010, this research is also jointly supported by the National Natural Science Foundation of China (Grant no. 61374048) and the National Priority Research Project NPRP 4-1162-1-181 funded by Qatar National Research Fund, Qatar. The authors wish to thank Dr. Shiping Wen for helpful discussions.

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Authors and Affiliations

  1. College of Computer, Chongqing University, Chongqing, 400044, China

    Ling Chen, Chuandong Li & Xin Wang

  2. Texas A&M University at Qatar, P.O. Box 23874, Doha, Qatar

    Tingwen Huang

  3. Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, 15261, USA

    Yiran Chen

Authors
  1. Ling Chen
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  2. Chuandong Li
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  3. Tingwen Huang
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  4. Yiran Chen
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  5. Xin Wang
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Corresponding author

Correspondence to Chuandong Li.

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Chen, L., Li, C., Huang, T. et al. Memristor crossbar-based unsupervised image learning. Neural Comput & Applic 25, 393–400 (2014). https://doi.org/10.1007/s00521-013-1501-0

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  • DOI: https://doi.org/10.1007/s00521-013-1501-0

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