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

@article{SerranoGotarredona2009CAVIARA4,
  title={CAVIAR: A 45k Neuron, 5M Synapse, 12G Connects/s AER Hardware Sensory–Processing– Learning–Actuating System for High-Speed Visual Object Recognition and Tracking},
  author={Rafael Serrano-Gotarredona and Matthias Oster and Patrick Lichtsteiner and Alejandro Linares-Barranco and Rafael Paz-Vicente and Francisco Gomez-Rodriguez and Luis A. Camu{\~n}as-Mesa and Raphael Berner and Manuel Rivas P{\'e}rez and Tobi Delbr{\"u}ck and Shih-Chii Liu and Rodney J. Douglas and Philipp H{\"a}fliger and Gabriel Jim{\'e}nez-Moreno and Antonio Abad Civit Balcells and Teresa Serrano-Gotarredona and Antonio Acosta-Jim{\'e}nez and Bernab{\'e} Linares-Barranco},
  journal={IEEE Transactions on Neural Networks},
  year={2009},
  volume={20},
  pages={1417-1438}
}
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 asynchronous 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, 45 k neurons (spiking cells), up to 5 M synapses, performs 12 G… 
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TLDR
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TLDR
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TLDR
A software simulator able to gather several spike-based elements into the same workspace in order to test a CA architecture based on AER before a hardware implementation, and produces VHDL for testing the AER-CA into the FPGA of the USBAER A ER-tool.
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TLDR
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TLDR
Key aspects in the proposed approach include utilizing the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable TP and the use of asynchronous frames for information binding.
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TLDR
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