On the one hand there is a monster that represents the Von...

On the one hand there is a monster that represents the Von Neumann solution.

On the other there is AKIDA (independently verified by Tata Consulting Services) that represents a new paradigm.

Low Power & Low LatencyCloud Cover Detection in Small

Satellites Using On-board NeuromorphicProcessors

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Chetan Kadway; Sounak Dey; Arijit Mukherjee; Arpan Pal; Gilles Bézard

All Authors

Abstract:

Emergence of small satellites for earth observation missions has opened up new horizons for space research but at the same time posed newer challenges of limited power and compute resource arising out of the size & weight constraints imposed by these satellites. The currently evolving neuromorphic computing paradigm shows promise in terms of energy efficiency and may possibly be exploited here. In this paper, we try to prove the applicability of neuromorphic computing for on-board data processing in satellites by creating a 2-stage hierarchical cloud cover detection application for multi-spectral earth observation images. Wedesign and train a CNN and convert it into SNN using the CNN2SNN conversiontoolkit of Brainchip Akida neuromorphic platform. We achieve 95.46% accuracywhile power consumption and latency are at least 35x and 3.4x more efficientrespectively in stage-1 (and 230x & 7x in stage-2) compared to theequivalent CNN running on Jetson TX2.

Published in: 2023International Joint Conference on Neural Networks (IJCNN)

https://ieeexplore.ieee.org/abstract/document/10191569

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