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GPU implementation of neural networks

K. Oh
School of Media, College of Information Science, Soongsil University, 1, SangDo-Dong, DongJak-Gu, Seoul, 156-743, Republic of Korea
Pattern Recognition, Vol. 37, No. 6. (June 2004), pp. 1311-1314

@article{oh2004gpu,

   title={GPU implementation of neural networks},

   author={Oh, K.S. and Jung, K.},

   journal={Pattern Recognition},

   volume={37},

   number={6},

   pages={1311–1314},

   year={2004},

   publisher={Elsevier}

}

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Graphics processing unit (GPU) is used for a faster artificial neural network. It is used to implement the matrix multiplication of a neural network to enhance the time performance of a text detection system. Preliminary results produced a 20-fold performance enhancement using an ATI RADEON 9700 PRO board. The parallelism of a GPU is fully utilized by accumulating a lot of input feature vectors and weight vectors, then converting the many inner-product operations into one matrix operation. Further research areas include benchmarking the performance with various hardware and GPU-aware learning algorithms.
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