Presentation
Spike-Based Convolutional Network for real-time processing
Author/s | Pérez Carrasco, José Antonio
Serrano Gotarredona, María del Carmen Acha Piñero, Begoña Serrano Gotarredona, María Teresa Linares Barranco, Bernabé |
Department | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Publication Date | 2010 |
Deposit Date | 2020-10-29 |
Published in |
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ISBN/ISSN | 978-1-4244-7542-1 1051-4651 |
Abstract | In this paper we propose the first bio-inspired sixlayer
convolutional network (ConvNet) non-frame based that
can be implemented with already physically available spikebased
electronic devices. The system was designed ... In this paper we propose the first bio-inspired sixlayer convolutional network (ConvNet) non-frame based that can be implemented with already physically available spikebased electronic devices. The system was designed to recognize people in three different positions: standing, lying or up-sidedown. The inputs were spikes obtained with a motion retina chip. We provide simulation results showing recognition delays of 16 milliseconds from stimulus onset (time-to-first spike) with a recognition rate of 94%. The weight sharing property in ConvNets and the use of AER protocol allow a great reduction in the number of both trainable parameters and connections (only 748 trainable parameters and 123 connections in our AER system (out of 506998 connections that would be required in a frame-based implementation). |
Funding agencies | Ministerio de Educación y Ciencia (MEC). España Junta de Andalucía |
Project ID. | TEC2006-11730-C03-01
P06-TIC-01417 |
Citation | Pérez Carrasco, J.A., Serrano Gotarredona, M.d.C., Acha Piñero, B., Serrano Gotarredona, M.T. y Linares Barranco, B. (2010). Spike-Based Convolutional Network for real-time processing. En ICPR 2010: 20th International Conference on Pattern Recognition (3085-3088), Istanbul, Turkey: IEEE Computer Society. |
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