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Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing

Author

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  • Guangdong Zhou

    (Southwest University)

  • Jie Li

    (Southern University of Science and Technology)

  • Qunliang Song

    (Southwest University)

  • Lidan Wang

    (Southwest University)

  • Zhijun Ren

    (Southwest University)

  • Bai Sun

    (Xi’an Jiaotong University)

  • Xiaofang Hu

    (Southwest University)

  • Wenhua Wang

    (Southwest University)

  • Gaobo Xu

    (Southwest University)

  • Xiaodie Chen

    (The University of Hong Kong)

  • Lan Cheng

    (Southwest University)

  • Feichi Zhou

    (Southern University of Science and Technology)

  • Shukai Duan

    (Southwest University)

Abstract

In-sensor and near-sensor computing are becoming the next-generation computing paradigm for high-density and low-power sensory processing. To fulfil a high-density and efficient neuromorphic visual system with fully hierarchical emulation of the retina and visual cortex, emerging multimodal neuromorphic devices for multi-stage processing and a fully hardware-implemented system with versatile image processing functions are still lacking and highly desirable. Here we demonstrate an emerging multimodal-multifunctional resistive random-access memory (RRAM) device array based on modified silk fibroin protein (MSFP), exhibiting both optoelectronic RRAM (ORRAM) mode featured by unique negative and positive photoconductance memory and electrical RRAM (ERRAM) mode featured by analogue resistive switching. A full hardware implementation of the artificial visual system with versatile image processing functions is realised for the first time, including ORRAM mode array for the in-sensor image pre-processing (contrast enhancement, background denoising, feature extraction) and ERRAM mode array for near-sensor high-level image recognition, which hugely improves the integration density, and simply the circuit design and the fabrication and integration complexity.

Suggested Citation

  • Guangdong Zhou & Jie Li & Qunliang Song & Lidan Wang & Zhijun Ren & Bai Sun & Xiaofang Hu & Wenhua Wang & Gaobo Xu & Xiaodie Chen & Lan Cheng & Feichi Zhou & Shukai Duan, 2023. "Full hardware implementation of neuromorphic visual system based on multimodal optoelectronic resistive memory arrays for versatile image processing," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43944-2
    DOI: 10.1038/s41467-023-43944-2
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    References listed on IDEAS

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    Cited by:

    1. Pengshan Xie & Yunchao Xu & Jingwen Wang & Dengji Li & Yuxuan Zhang & Zixin Zeng & Boxiang Gao & Quan Quan & Bowen Li & You Meng & Weijun Wang & Yezhan Li & Yan Yan & Yi Shen & Jia Sun & Johnny C. Ho, 2024. "Birdlike broadband neuromorphic visual sensor arrays for fusion imaging," Nature Communications, Nature, vol. 15(1), pages 1-12, December.

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