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Pruning and quantization algorithm with applications in memristor-based convolutional neural network

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Abstract

The human brain’s ultra-low power consumption and highly parallel computational capabilities can be accomplished by memristor-based convolutional neural networks. However, with the rapid development of memristor-based convolutional neural networks in various fields, more complex applications and heavier computations lead to the need for a large number of memristors, which makes power consumption increase significantly and the network model larger. To mitigate this problem, this paper proposes an SBT-memristor-based convolutional neural network architecture and a hybrid optimization method combining pruning and quantization. Firstly, SBT-memristor-based convolutional neural network is constructed by using the good thresholding property of the SBT memristor. The memristive in-memory computing unit, activation unit and max-pooling unit are designed. Then, the hybrid optimization method combining pruning and quantization is used to improve the SBT-memristor-based convolutional neural network architecture. This hybrid method can simplify the memristor-based neural network and represent the weights at the memristive synapses better. Finally, the results show that the SBT-memristor-based convolutional neural network reduces a large number of memristors, decreases the power consumption and compresses the network model at the expense of a little precision loss. The SBT-memristor-based convolutional neural network obtains faster recognition speed and lower power consumption in MNIST recognition. It provides new insights for the complex application of convolutional neural networks.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant Nos. 62176143, 61703246), the Natural Science Foundation of Shandong Province (ZR2022MF225, ZR2021MF001), the Talented Young Teachers Training Program of Shandong University of Science and Technology, and the Elite Project of Shandong University of Science and Technology.

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Authors and Affiliations

  1. College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao, 266590, China

    Mei Guo, Yurui Sun, Yongliang Zhu & Gang Dou

  2. The University of Nottingham Ningbo China, Ningbo, 315100, China

    Mingqiao Han

  3. The Australian AI Institute, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW, 2007, Australia

    Shiping Wen

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  1. Mei Guo
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  2. Yurui Sun
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  4. Mingqiao Han
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  5. Gang Dou
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Correspondence to Gang Dou.

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Guo, M., Sun, Y., Zhu, Y. et al. Pruning and quantization algorithm with applications in memristor-based convolutional neural network. Cogn Neurodyn 18, 233–245 (2024). https://doi.org/10.1007/s11571-022-09927-7

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