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Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-32048

Serrano-Gotarredona, R; Oster, M; Lichtsteiner, P; Linares-Barranco, A; Paz-Vicente, R; Gomez-Rodriguez, F; Camuñas-Mesa, L; Berner, R; Rivas, M.; Delbruck, T; Liu, S C; Douglas, R J; Hafliger, P; Moreno, G; Civit, A; Serrano-Gotarredona, T; Acosta-Jimenez, A; Linares-Barranco, B (2009). CAVIAR: a 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing-learning-actuating system for high-speed visual object recognition and tracking. IEEE Transactions on Neural Networks, 20(9):1417-1438.

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Abstract

This paper describes CAVIAR, a massively parallel hardware implementation of a spike-based sensing-processing-learning-actuating system inspired by the physiology of the nervous system. CAVIAR uses the asychronous address-event representation (AER) communication framework and was developed in the context of a European Union funded project. It has four custom mixed-signal AER chips, five custom digital AER interface components, 45k neurons (spiking cells), up to 5M synapses, performs 12G synaptic operations per second, and achieves millisecond object recognition and tracking latencies.

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56 citations in Web of Science®
105 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
DDC:570 Life sciences; biology
Uncontrolled Keywords:neuromorphic AER system
Language:English
Date:2009
Deposited On:06 Mar 2010 15:47
Last Modified:28 Nov 2013 01:51
Publisher:IEEE
ISSN:1045-9227
Additional Information:© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Publisher DOI:10.1109/TNN.2009.2023653
Related URLs:http://www.ini.uzh.ch/node/21375 (Organisation)
PubMed ID:10.1109/TNN.2009.2023653

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