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Proceedings Paper

Hardware-based artificial neural networks for size, weight, and power constrained platforms
Author(s): B. T. Wysocki; N. R. McDonald; C. D. Thiem
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Paper Abstract

A fully parallel, silicon-based artificial neural network (CM1K) built on zero instruction set computer (ZISC) technology was used for change detection and object identification in video data. Fundamental pattern recognition capabilities were demonstrated with reduced neuron numbers utilizing only a few, or in some cases one, neuron per category. This simplified approach was used to validate the utility of few neuron networks for use in applications that necessitate severe size, weight, and power (SWaP) restrictions. The limited resource requirements and massively parallel nature of hardware-based artificial neural networks (ANNs) make them superior to many software approaches in resource limited systems, such as micro-UAVs, mobile sensor platforms, and pocket-sized robots.

Paper Details

Date Published: 22 May 2014
PDF: 8 pages
Proc. SPIE 9119, Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII, 911909 (22 May 2014); doi: 10.1117/12.2052440
Show Author Affiliations
B. T. Wysocki, Air Force Research Lab. (United States)
N. R. McDonald, Air Force Research Lab. (United States)
C. D. Thiem, Air Force Research Lab. (United States)

Published in SPIE Proceedings Vol. 9119:
Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII
Misty Blowers; Jonathan Williams, Editor(s)

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