In-sensor image memorization and encoding via optical neurons for bio-stimulus domain reduction toward visual cognitive processing

Doeon Lee, Minseong Park, Yongmin Baek, Byungjoon Bae, Junseok Heo () and Kyusang Lee ()
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Doeon Lee: University of Virginia
Minseong Park: University of Virginia
Yongmin Baek: University of Virginia
Byungjoon Bae: University of Virginia
Junseok Heo: Ajou University
Kyusang Lee: University of Virginia

Nature Communications, 2022, vol. 13, issue 1, 1-9

Abstract: Abstract As machine vision technology generates large amounts of data from sensors, it requires efficient computational systems for visual cognitive processing. Recently, in-sensor computing systems have emerged as a potential solution for reducing unnecessary data transfer and realizing fast and energy-efficient visual cognitive processing. However, they still lack the capability to process stored images directly within the sensor. Here, we demonstrate a heterogeneously integrated 1-photodiode and 1 memristor (1P-1R) crossbar for in-sensor visual cognitive processing, emulating a mammalian image encoding process to extract features from the input images. Unlike other neuromorphic vision processes, the trained weight values are applied as an input voltage to the image-saved crossbar array instead of storing the weight value in the memristors, realizing the in-sensor computing paradigm. We believe the heterogeneously integrated in-sensor computing platform provides an advanced architecture for real-time and data-intensive machine-vision applications via bio-stimulus domain reduction.

Date: 2022
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DOI: 10.1038/s41467-022-32790-3

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