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Non-spike timing-dependent plasticity learning mechanism for memristive neural networks

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Abstract

Memristive neural networks (MNNs) attract the attention of many researchers because memristor can mimic the learning mechanism of biologic neuron, spike timing-dependent plasticity (STDP). While STDP brings huge potentials on many applications for memristive neural networks, it also gives complex calculation process for hardware implement. In this work, a non-STDP learning mechanism is proposed, which is implemented in two common frameworks including feedforward neural network and crossbar. The non-STDP learning mechanism relies on the linear relationship between the value of memristor and area of input spikes, which gives the proposed method a simple calculation process and better hardware compatibility. Experimental results show that the non-STDP learning mechanism can help to achieve good hardware performance in both feedforward neural network and crossbar frameworks. Compared with STDP based memristive neural networks, the proposed method can save 2.19%-24.4% hardware resource (ALMs) and improve 1.56-12.25 MHz processing speed under a set of different network scales. In future, some other complex memristor models with non-STDP learning mechanism should be taken into consideration, which will give more room for practical applications of memristive neural networks.

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Author information

Authors and Affiliations

  1. School of Physics and Technology, Wuhan University, Wuhan, China

    Zhiri Tang

  2. Department of Computer Science, City University of Hong Kong, Hong Kong, China

    Zhiri Tang & Zhihua Wang

  3. Department of Geography, The University of Hong Kong, Hong Kong, China

    Yanhua Chen

  4. Guangdong Key Laboratory of Modern Control Technology, Guangdong Institute of Intelligent Manufacturing, Guangzhou, China

    Ruihan Hu

  5. Department of Automation, Shanghai Jiao Tong University, Shanghai, China

    Edmond Q. Wu

Authors
  1. Zhiri Tang
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  2. Yanhua Chen
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  3. Zhihua Wang
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  4. Ruihan Hu
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  5. Edmond Q. Wu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Yanhua Chen, Zhihua Wang, Ruihan Hu, and Edmond Q. Wu. The first draft of the manuscript was written by Zhiri Tang and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Ruihan Hu.

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Tang, Z., Chen, Y., Wang, Z. et al. Non-spike timing-dependent plasticity learning mechanism for memristive neural networks. Appl Intell 51, 3684–3695 (2021). https://doi.org/10.1007/s10489-020-01985-w

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