Abstract
Although Graphics Processing Units (GPUs) seem to currently be the best platform to train machine learning models, most research laboratories are still only equipped with standard CPU systems. In this paper, we investigate multiple techniques to speedup the training of Restricted Boltzmann Machine (RBM) models and Convolutional RBM (CRBM) models on CPU with the Contrastive Divergence (CD) algorithm. Experimentally, we show that the proposed techniques can reduce the training time by up to 30 times for RBM and up to 12 times for CRBM, on a data set of handwritten digits.
Cite
CITATION STYLE
Wicht, B., Fischer, A., & Hennebert, J. (2016). On CPU performance optimization of restricted Boltzmann machine and Convolutional RBM. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9896 LNAI, pp. 163–174). Springer Verlag. https://doi.org/10.1007/978-3-319-46182-3_14
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