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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Detalles 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 recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2009
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/75028
Acceso en línea:https://hdl.handle.net/11441/75028
https://doi.org/10.1109/TNN.2009.2023653
Access Level:acceso abierto
Palabra clave:Address–event representation (AER)
Neuromorphic chips
Neuromorphic systems
Vision
Descripción
Sumario: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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