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Hardware based spatio-temporal neural processing backend for imaging sensors: towards a smart camera
Author(s): Samiran Ganguly; Yunfei Gu; Mircea R. Stan; Avik W. Ghosh
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Paper Abstract

In this work we show how we can build a technology platform for cognitive imaging sensors using recent advances in recurrent neural network architectures and training methods inspired from biology. We demonstrate learning and processing tasks specific to imaging sensors, including enhancement of sensitivity and signal-to-noise ratio (SNR) purely through neural filtering beyond the fundamental limits sensor materials, and inferencing and spatio-temporal pattern recognition capabilities of these networks with applications in object detection, motion tracking and prediction. We then show designs of unit hardware cells built using complementary metal-oxide semiconductor (CMOS) and emerging materials technologies for ultra-compact and energy-efficient embedded neural processors for smart cameras.

Paper Details

Date Published: 14 May 2018
PDF: 11 pages
Proc. SPIE 10656, Image Sensing Technologies: Materials, Devices, Systems, and Applications V, 106560Z (14 May 2018); doi: 10.1117/12.2305137
Show Author Affiliations
Samiran Ganguly, Univ. of Virginia (United States)
Yunfei Gu, Univ. of Virginia (United States)
Mircea R. Stan, Univ. of Virginia (United States)
Avik W. Ghosh, Univ. of Virginia (United States)


Published in SPIE Proceedings Vol. 10656:
Image Sensing Technologies: Materials, Devices, Systems, and Applications V
Nibir K. Dhar; Achyut K. Dutta, Editor(s)

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