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

Guangdong Zhou, Jie Li, Qunliang Song, Lidan Wang, Zhijun Ren, Bai Sun, Xiaofang Hu, Wenhua Wang, Gaobo Xu, Xiaodie Chen, Lan Cheng, Feichi Zhou () and Shukai Duan ()
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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

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-43944-2

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