Neural Network Implementation Using CUDA and OpenMP
DICTA '08 Proceedings of the 2008 Digital Image Computing: Techniques and Applications
Irregularity detection on low tension electric installations by neural network ensembles
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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Training feed-forward neural networks can take a long time when there is a large amount of data to be used, even when training with more efficient algorithms like Levenberg-Marquardt. Parallel architectures have been a common solution in the area of high performance computing, since the technology used in current processors is reaching the limits of speed. An architecture that has been gaining popularity is the GPGPU (General-Purpose computing on Graphics Processing Units), which has received large investments from companies such as NVIDIA that introduced CUDA (Compute Unified Device Architecture) technology. This paper proposes a faster implementation of neural networks training with Levenberg-Marquardt algorithm using CUDA. The results obtained demonstrate that the whole training time can be almost 30 times shorter than code using Intel Math Library (MKL). A case study for classifying electrical company customers is presented.