Ponencia
Red neuronal convolucional rápida sin fotogramas para reconocimientos de dígitos
Autor/es | Pérez Carrasco, José Antonio
Serrano Gotarredona, María del Carmen Acha Piñero, Begoña Serrano Gotarredona, María Teresa Linares Barranco, Bernabé |
Departamento | Universidad de Sevilla. Departamento de Teoría de la Señal y Comunicaciones Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2011 |
Fecha de depósito | 2018-10-10 |
Publicado en |
|
Resumen | In this paper a bio-inspired six-layer convolutional
network (ConvNet) non-frame based for digit recognition is
shown. The system has been trained with the backpropagation
algorithm using 32x32 images from the MNIST ... In this paper a bio-inspired six-layer convolutional network (ConvNet) non-frame based for digit recognition is shown. The system has been trained with the backpropagation algorithm using 32x32 images from the MNIST database. The system can be implemented with already physically available spike-based electronic devices. 10000 images have been coded into events separated 50ns to test the non-frame based ConvNet system. The simulation results have been obtained using actual performance figures for existing AER (Address Event Representation) hardware components. We provide simulation results of the system showing recognition delays of a few microseconds from stimulus onset with a recognition rate of 93%. The complete system consists of 30 convolution modules. |
Agencias financiadoras | Ministerio de Ciencia e Innovación (MICIN). España Junta de Andalucía |
Identificador del proyecto | TEC2009-10639-C04-01
P06-TIC-01417 |
Cita | Pérez Carrasco, J.A., Serrano Gotarredona, M.d.C., Acha Piñero, B., Serrano Gotarredona, M.T. y Linares Barranco, B. (2011). Red neuronal convolucional rápida sin fotogramas para reconocimientos de dígitos. En XXVI Simposio de la URSI (1-4), Leganés (España): Unión Científica Internacional de Radio. |
Ficheros | Tamaño | Formato | Ver | Descripción |
---|---|---|---|---|
RED NEURONAL.pdf | 275.1Kb | [PDF] | Ver/ | |