CAVIAR: A 45k Neuron, 5M Synapse, 12G Connects/s AER Hardware Sensory-Processing- Learning-Actuating System for High-Speed Visual Object Recognition and Tracking

نویسندگان

  • Rafael Serrano-Gotarredona
  • Matthias Oster
  • Patrick Lichtsteiner
  • Alejandro Linares-Barranco
  • Rafael Paz-Vicente
  • Francisco Gomez-Rodriguez
  • Luis A. Camuñas-Mesa
  • Raphael Berner
  • Manuel Rivas Pérez
  • Tobi Delbrück
  • Shih-Chii Liu
  • Rodney J. Douglas
  • Philipp Häfliger
  • Gabriel Jiménez-Moreno
  • Antonio Abad Civit Balcells
  • Teresa Serrano-Gotarredona
  • Antonio Acosta-Jimenez
  • Bernabé Linares-Barranco
چکیده

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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عنوان ژورنال:
  • IEEE transactions on neural networks

دوره 20 9  شماره 

صفحات  -

تاریخ انتشار 2009