Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89129
Title: Handwritten-digit recognition by hybrid convolutional neural network based on Hfo2 memristive spiking-neuron
Authors: Yin, Y.
Chen, Tu Pei
Hosoka, Sumio
Liu, Y.
Wang, J. J.
Hu, S. G.
Zhan, X. T.
Yu, Q.
Liu, Z.
Keywords: Neural Network
Handwritten-digit Recognition
DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Wang, J. J., Hu, S. G., Zhan, X. T., Yu, Q., Liu, Z., Chen, T. P., . . . Liu, Y. (2018). Handwritten-digit recognition by hybrid convolutional neural network based on Hfo2 memristive spiking-neuron. Scientific Reports, 8, 12546-. doi:10.1038/s41598-018-30768-0
Series/Report no.: Scientific Reports
Abstract: Although there is a huge progress in complementary-metal-oxide-semiconductor (CMOS) technology, construction of an artificial neural network using CMOS technology to realize the functionality comparable with that of human cerebral cortex containing 1010–1011 neurons is still of great challenge. Recently, phase change memristor neuron has been proposed to realize a human-brain level neural network operating at a high speed while consuming a small amount of power and having a high integration density. Although memristor neuron can be scaled down to nanometer, integration of 1010–1011 neurons still faces many problems in circuit complexity, chip area, power consumption, etc. In this work, we propose a CMOS compatible HfO2 memristor neuron that can be well integrated with silicon circuits. A hybrid Convolutional Neural Network (CNN) based on the HfO2 memristor neuron is proposed and constructed. In the hybrid CNN, one memristive neuron can behave as multiple physical neurons based on the Time Division Multiplexing Access (TDMA) technique. Handwritten digit recognition is demonstrated in the hybrid CNN with a memristive neuron acting as 784 physical neurons. This work paves the way towards substantially shrinking the amount of neurons required in hardware and realization of more complex or even human cerebral cortex level memristive neural networks.
URI: https://hdl.handle.net/10356/89129
http://hdl.handle.net/10220/46115
ISSN: 2045-2322
DOI: 10.1038/s41598-018-30768-0
Rights: © 2018 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

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