As a superior data analysis method, Machine Learning suffers the bottleneck from limited computing capability for many years. With the advent of numerous parallel computing hardwares, modern GPU is becoming a promising carrier for the tasks of Machine Learning. In this paper, we propose an efficient GPU execution framework to speedup the forward propagation process of convolution neural network. By extending the convolution unrolling method to fit this batch mode, we get a significant increase of throughput but very little overhead. © 2014 IFIP International Federation for Information Processing.
CITATION STYLE
Gao, Y., Wang, R., An, N., Wei, Y., & Qian, D. (2014). Speedup critical stage of machine learning with batch scheduling in GPU. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8707 LNCS, pp. 522–525). Springer Verlag. https://doi.org/10.1007/978-3-662-44917-2_43
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