A pipeline Hardware Implementation for an Artificial Neural Network

Denis F. WolfGedson FariaRoseli A. F. RomeroEduardo MarquesMarco A. TeixeiraAlexandre A. L. RibeiroLeandro C. FernandesJean M. ScatenaRovilson Mezencio

Artificial Neural Networks are computational devices inspired by the human brain for solving problems. Currently, they are being widely applied for solving problems in several areas such as: robotics, image processing, pattern recognition, etc.... The neural network model, Multilayer Perceptrons, is one of the most used due to its simple learning algorithm. However, its convergence is very slow. To take advantage of the massive parallelism inherent to this model, a hardware parallel implementation should be performed. There are different hardware parallel implementations for this particular model. This paper presents a reconfigurable hardware parallel implementation for Multilayer Perceptrons by using pipelines. Tests realized showed that the use of pipelines speeded up the execution time of the hardware parallel implementation. Clique no link abaixo para buscar o texto completo deste trabalho na Web: Buscar na Web

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