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Neuromorphic networks on the SpiNNaker platform


Haessig, Germain; Galluppi, Francesco; Lagorce, Xavier; Benosman, Ryad (2019). Neuromorphic networks on the SpiNNaker platform. In: 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Hsinchu, Taiwan, 18 March 2019 - 20 March 2019.

Abstract

This paper describes spike-based neural networks for optical flow and stereo estimation from Dynamic Vision Sensors data. These methods combine the Asynchronous Time-based Image Sensor with the SpiNNaker platform. The sensor generates spikes with sub-millisecond resolution in response to scene illumination changes. These spike are processed by a spiking neural network running on SpiNNaker with a 1 millisecond resolution to accurately determine the order and time difference of spikes from neighboring pixels, and therefore infer the velocity, direction or depth. The spiking neural networks are a variant of the Barlow-Levick method for optical flow estimation, and Marr& Poggio for the stereo matching.

Abstract

This paper describes spike-based neural networks for optical flow and stereo estimation from Dynamic Vision Sensors data. These methods combine the Asynchronous Time-based Image Sensor with the SpiNNaker platform. The sensor generates spikes with sub-millisecond resolution in response to scene illumination changes. These spike are processed by a spiking neural network running on SpiNNaker with a 1 millisecond resolution to accurately determine the order and time difference of spikes from neighboring pixels, and therefore infer the velocity, direction or depth. The spiking neural networks are a variant of the Barlow-Levick method for optical flow estimation, and Marr& Poggio for the stereo matching.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:20 March 2019
Deposited On:11 Feb 2020 15:10
Last Modified:16 Feb 2020 07:08
Publisher:IEEE
ISBN:9781538678848
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/aicas.2019.8771512

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