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Memristor-based storage system with convolutional autoencoder-based image compression network

Author

Listed:
  • Yulin Feng

    (Peking University
    Beijing Information Science & Technology University)

  • Yizhou Zhang

    (Peking University)

  • Zheng Zhou

    (Peking University)

  • Peng Huang

    (Peking University)

  • Lifeng Liu

    (Peking University)

  • Xiaoyan Liu

    (Peking University)

  • Jinfeng Kang

    (Peking University)

Abstract

The exponential growth of various complex images is putting tremendous pressure on storage systems. Here, we propose a memristor-based storage system with an integrated near-storage in-memory computing-based convolutional autoencoder compression network to boost the energy efficiency and speed of the image compression/retrieval and improve the storage density. We adopt the 4-bit memristor arrays to experimentally demonstrate the functions of the system. We propose a step-by-step quantization aware training scheme and an equivalent transformation for transpose convolution to improve the system performance. The system exhibits a high (>33 dB) peak signal-to-noise ratio in the compression and decompression of the ImageNet and Kodak24 datasets. Benchmark comparison results show that the 4-bit memristor-based storage system could reduce the latency and energy consumption by over 20×/5.6× and 180×/91×, respectively, compared with the server-grade central processing unit-based/the graphics processing unit-based processing system, and improve the storage density by more than 3 times.

Suggested Citation

  • Yulin Feng & Yizhou Zhang & Zheng Zhou & Peng Huang & Lifeng Liu & Xiaoyan Liu & Jinfeng Kang, 2024. "Memristor-based storage system with convolutional autoencoder-based image compression network," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-45312-0
    DOI: 10.1038/s41467-024-45312-0
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    References listed on IDEAS

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    1. Mingyi Rao & Hao Tang & Jiangbin Wu & Wenhao Song & Max Zhang & Wenbo Yin & Ye Zhuo & Fatemeh Kiani & Benjamin Chen & Xiangqi Jiang & Hefei Liu & Hung-Yu Chen & Rivu Midya & Fan Ye & Hao Jiang & Zhong, 2023. "Thousands of conductance levels in memristors integrated on CMOS," Nature, Nature, vol. 615(7954), pages 823-829, March.
    2. Peng Yao & Huaqiang Wu & Bin Gao & Jianshi Tang & Qingtian Zhang & Wenqiang Zhang & J. Joshua Yang & He Qian, 2020. "Fully hardware-implemented memristor convolutional neural network," Nature, Nature, vol. 577(7792), pages 641-646, January.
    3. Zhengwu Liu & Jianshi Tang & Bin Gao & Peng Yao & Xinyi Li & Dingkun Liu & Ying Zhou & He Qian & Bo Hong & Huaqiang Wu, 2020. "Neural signal analysis with memristor arrays towards high-efficiency brain–machine interfaces," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
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