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Pattern classification by memristive crossbar circuits using ex situ and in situ training

Author

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  • Fabien Alibart

    (University of California at Santa Barbara
    Present address: Institut d’Electronique de Micro et Nanotechnologie (IEMN), Centre National de la Recherche Scientifique (CNRS), Villeneuve d’Ascq, 59652 Cedex, France)

  • Elham Zamanidoost

    (University of California at Santa Barbara)

  • Dmitri B. Strukov

    (University of California at Santa Barbara)

Abstract

Memristors are memory resistors that promise the efficient implementation of synaptic weights in artificial neural networks. Whereas demonstrations of the synaptic operation of memristors already exist, the implementation of even simple networks is more challenging and has yet to be reported. Here we demonstrate pattern classification using a single-layer perceptron network implemented with a memrisitive crossbar circuit and trained using the perceptron learning rule by ex situ and in situ methods. In the first case, synaptic weights, which are realized as conductances of titanium dioxide memristors, are calculated on a precursor software-based network and then imported sequentially into the crossbar circuit. In the second case, training is implemented in situ, so the weights are adjusted in parallel. Both methods work satisfactorily despite significant variations in the switching behaviour of the memristors. These results give hope for the anticipated efficient implementation of artificial neuromorphic networks and pave the way for dense, high-performance information processing systems.

Suggested Citation

  • Fabien Alibart & Elham Zamanidoost & Dmitri B. Strukov, 2013. "Pattern classification by memristive crossbar circuits using ex situ and in situ training," Nature Communications, Nature, vol. 4(1), pages 1-7, October.
    • Handle: RePEc:nat:natcom:v:4:y:2013:i:1:d:10.1038_ncomms3072
    DOI: 10.1038/ncomms3072
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    Cited by:

    1. Alonso, F.J. & Maldonado, D. & Aguilera, A.M. & Roldán, J.B., 2021. "Memristor variability and stochastic physical properties modeling from a multivariate time series approach," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    2. Lee, Geun Ho & Kim, Tae-Hyeon & Song, Min Suk & Park, Jinwoo & Kim, Sungjoon & Hong, Kyungho & Kim, Yoon & Park, Byung-Gook & Kim, Hyungjin, 2022. "Effect of weight overlap region on neuromorphic system with memristive synaptic devices," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).
    3. Maldonado, D. & Aguilera-Pedregosa, C. & Vinuesa, G. & García, H. & Dueñas, S. & Castán, H. & Aldana, S. & González, M.B. & Moreno, E. & Jiménez-Molinos, F. & Campabadal, F. & Roldán, J.B., 2022. "An experimental and simulation study of the role of thermal effects on variability in TiN/Ti/HfO2/W resistive switching nonlinear devices," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    4. María José Ibáñez & Domingo Barrera & David Maldonado & Rafael Yáñez & Juan Bautista Roldán, 2021. "Non-Uniform Spline Quasi-Interpolation to Extract the Series Resistance in Resistive Switching Memristors for Compact Modeling Purposes," Mathematics, MDPI, vol. 9(17), pages 1-12, September.
    5. Boyuan Cui & Zhen Fan & Wenjie Li & Yihong Chen & Shuai Dong & Zhengwei Tan & Shengliang Cheng & Bobo Tian & Ruiqiang Tao & Guo Tian & Deyang Chen & Zhipeng Hou & Minghui Qin & Min Zeng & Xubing Lu & , 2022. "Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    6. Li, Liangchen & Xu, Rui & Lin, Jiazhe, 2020. "Lagrange stability for uncertain memristive neural networks with Lévy noise and leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).

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