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A 3.0$_\mu$W@5fps QQVGA self-controlled wake-up imager with on-chip motion detection, auto-exposure and object recognition

Abstract : Analyzing image content usually comes at the expense of a power consumption incompatible with battery-powered systems. Aiming at proposing a solution to this problem, this paper presents an imager with full on-chip object recognition, consuming sub-10$\mu$W using standard 4T pixels in 90nm imaging CMOS technology, opening the path for both wakeup and high-quality imaging. It combines multi-modality event-of-interest detection with self-controlled capabilities, a key for low-power applications. It embeds a log-domain autoexposure algorithm to increase on-chip automation. The power consumption figures range from 3.0 to 5.7$\mu$W at 5fps for a QQVGA resolution while enabling background subtraction and single-scale object recognition. This typically shows a measured 94% accuracy for a face detection use case.
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https://hal-cea.archives-ouvertes.fr/cea-02903508
Contributor : William Guicquero <>
Submitted on : Tuesday, July 21, 2020 - 11:08:14 AM
Last modification on : Thursday, July 23, 2020 - 3:27:16 AM

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Arnaud Verdant, William Guicquero, Nicolas Royer, Guillaume Moritz, Sébastien Martin, et al.. A 3.0$_\mu$W@5fps QQVGA self-controlled wake-up imager with on-chip motion detection, auto-exposure and object recognition. 2020 Symposia on VLSI Technology and Circuits, Jun 2020, Honolulu, HI, United States. ⟨cea-02903508⟩

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