CUDA-enabled implementation of a neural network algorithm for handwritten digit recognition

Authors:
P. Yu. Izotov;N. L. Kazanskiy;D. L. Golovashkin;S. V. Sukhanov
Affiliations:
Image Processing Systems Institute of the RAS, Samara, Russia 443001 and S.P. Korolyov Samara State Aerospace University, Samara, Russia;Image Processing Systems Institute of the RAS, Samara, Russia 443001 and S.P. Korolyov Samara State Aerospace University, Samara, Russia;Image Processing Systems Institute of the RAS, Samara, Russia 443001 and S.P. Korolyov Samara State Aerospace University, Samara, Russia;S.P. Korolyov Samara State Aerospace University, Samara, Russia
Venue:
Optical Memory and Neural Networks
Year:
2011

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Abstract

Using a convolutional neural network as an example, we discuss specific aspects of implementing a learning algorithm of pattern recognition on the GPU graphics card using NVIDIA CUDA architecture. The training time of the neural network on a video-adapter is decreased by a factor of 5.96 and the recognition time of a test set is decreased by a factor of 8.76 when compared with the implementation of an optimized algorithm on a central processing unit (CPU). We show that the implementation of the neural network algorithms on graphics processors holds promise.