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Neural Computing and Applications

, Volume 30, Issue 2, pp 503–508 | Cite as

Design of memristor-based image convolution calculation in convolutional neural network

  • Xiaofen Zeng
  • Shiping Wen
  • Zhigang Zeng
  • Tingwen Huang
Original Article
  • 635 Downloads

Abstract

In this paper, an architecture based on memristors is proposed to implement image convolution computation in convolutional neural networks. This architecture could extract different features of input images when using different convolutional kernels. Bipolar memristors with threshold are employed in this work, which vary their conductance values under different voltages. Various kernels are needed to extract information of input images, while different kernels contain different weights. The memristances of bipolar memristors with threshold are convenient to be varied and kept, which make them suitable to act as the weights of kernels. The performances of the design are verified by simulation results.

Keywords

Memristor Convolutional neural network Image convolution computation 

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Copyright information

© The Natural Computing Applications Forum 2016

Authors and Affiliations

  • Xiaofen Zeng
    • 1
    • 2
  • Shiping Wen
    • 1
    • 2
  • Zhigang Zeng
    • 1
    • 2
  • Tingwen Huang
    • 3
  1. 1.School of AutomationHuazhong University of Science and TechnologyWuhanPeople’s Republic of China
  2. 2.Key Laboratory of Image Processing and Intelligent Control of Education Ministry of ChinaWuhanPeople’s Republic of China
  3. 3.Texas A & M University at QatarDohaQatar

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