CAVIAR: A 45k neuron, 5M synapse, 12G connects/s AER hardware sensory-processing-learning-actuating system for high-speed visual object recognition and tracking

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 co...

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Detalhes bibliográficos
Autores: Serrano Gotarredona, Rafael, Oster, Matthias, Lichtsteiner, Patrick, Linares Barranco, Alejandro, Paz Vicente, Rafael, Gómez Rodríguez, Francisco de Asís, Camuñas Mesa, Luis Alejandro, Berner, Raphael, Rivas Pérez, Manuel, Jiménez Moreno, Gabriel, Civit Balcells, Antón, Serrano Gotarredona, María Teresa, Acosta Jiménez, Antonio José, Linares Barranco, Bernabé
Tipo de documento: artigo
Estado:Versión aceptada para publicación
Data de publicação:2009
País:España
Recursos:Universidad de Sevilla (US)
Repositório:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/75028
Acesso em linha:https://hdl.handle.net/11441/75028
https://doi.org/10.1109/TNN.2009.2023653
Access Level:Acceso aberto
Palavra-chave:Address–event representation (AER)
Neuromorphic chips
Neuromorphic systems
Vision
Descrição
Resumo: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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