Convolutional Neural Networks (CNNs) have been successfully used for many computer vision applications. It would be beneficial to these applications if the computational workload of CNNs could be reduced. In this work we analyze the linear algebraic properties of CNNs and propose an algorithmic modification to reduce their computational workload. An up to a 47% reduction can be achieved without any change in the image recognition results or the addition of any hardware accelerators. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Cong, J., & Xiao, B. (2014). Minimizing computation in convolutional neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8681 LNCS, pp. 281–290). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_36
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