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International Symposium on Neural Networks

ISNN 2015: Advances in Neural Networks – ISNN 2015 pp 166–173Cite as

Memristor Crossbar Array for Image Storing

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9377))

Abstract

This letter uses image overlay technique on memristor crossbar array (MCA) structure for image storing. Different programming circuits with time slot techniques are designed for the MCA consisting of the nonlinear HP memristor (HPMCA) and the MCA composed of the piece-wise linear threshold memristor (TMCA). The experiment results indicate that the HPMCA has a better performance, the TMCA is more practical in the industrial implementation. As a conclusion, the MCA made up of the memristor with both the nonlinear drift boundary property and the threshold property is preferred for image overlay.

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Author information

Authors and Affiliations

  1. The College of Electronic and Information Engineering, Southwest University, Chongqing, 400715, China

    Ling Chen & Chuandong Li

  2. Texas A & M University at Qatar, Doha, 5825, Qatar

    Tingwen Huang

  3. School of Automation, Huazhong University of Science and Technology, Wuhan, 430074, China

    Shiping Wen

  4. 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. Shiping Wen
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  5. Yiran Chen
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaolin Hu

  2. Fuzhou University, Fuzhou, China

    Yousheng Xia

  3. School of Information Science and Technology, Sun Yat-sen University, Guangzhou, China

    Yunong Zhang

  4. Chinese Academy of Sciences, Institute of Automation, Beijing, China

    Dongbin Zhao

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© 2015 Springer International Publishing Switzerland

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Cite this paper

Chen, L., Li, C., Huang, T., Wen, S., Chen, Y. (2015). Memristor Crossbar Array for Image Storing. In: Hu, X., Xia, Y., Zhang, Y., Zhao, D. (eds) Advances in Neural Networks – ISNN 2015. ISNN 2015. Lecture Notes in Computer Science(), vol 9377. Springer, Cham. https://doi.org/10.1007/978-3-319-25393-0_19

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  • DOI: https://doi.org/10.1007/978-3-319-25393-0_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25392-3

  • Online ISBN: 978-3-319-25393-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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