Parallel Training of a Back-Propagation Neural Network Using CUDA

The Artificial Neural Networks (ANN) training represents a time-consuming process in machine learning systems. In this work we provide an implementation of the back-propagation algorithm on CUDA, a parallel computing architecture developed by NVIDIA. Using CUBLAS, a CUDA implementation of the Basic...

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Bibliographic Details
Published in2010 International Conference on Machine Learning and Applications pp. 307 - 312
Main Authors Sierra-Canto, X, Madera-Ramirez, F, Uc-Cetina, V
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Artificial neural networks
back-propagation
CUDA
Distance measurement
Graphics processing unit
Instruction sets
Kernel
neural networks
Neurons
Training
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Summary:The Artificial Neural Networks (ANN) training represents a time-consuming process in machine learning systems. In this work we provide an implementation of the back-propagation algorithm on CUDA, a parallel computing architecture developed by NVIDIA. Using CUBLAS, a CUDA implementation of the Basic Linear Algebra Subprograms library (BLAS), the process is simplified, however, the use of kernels was necessary since CUBLAS does not have all the required operations. The implementation was tested with two standard benchmark data sets and the results show that the parallel training algorithm runs 63 times faster than its sequential version.
ISBN:1424492114
9781424492114
DOI:10.1109/ICMLA.2010.52