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oai:gredos.usal.es:10366/1351492025-05-29T07:29:14Zcom_10366_122575com_10366_4512com_10366_3823col_10366_134811

Raveane, William González Arrieta, María Angélica 2017-09-06T09:17:02Z 2017-09-06T09:17:02Z 2014-06 Distributed Computing and Artificial Intelligence, 11th International Conference. Advances in Intelligent Systems and Computing. Volumen 290, pp. 485-492. 978-3-319-07592-1(Print) / 978-3-319-07593-8(Online) 2194-5357(Print) / 2194-5365(Online) http://dx.doi.org/10.1007/978-3-319-07593-8_56 http://hdl.handle.net/10366/135149 We present a technique for improving the speed of a convolutional neural network applied to large input images through the optimization of the sliding window approach. Meaningful performance gains and memory bandwidth reduction can be obtained by processing images in this manner, factors which play a crucial role in the deployment of deep neural networks within mobile devices. application/pdf en Springer Science + Business Media Attribution-NonCommercial-NoDerivs 3.0 Unported https://creativecommons.org/licenses/by-nc-nd/3.0/ info:eu-repo/semantics/openAccess Computer Science Shared Map Convolutional Neural Networks for Real-Time Mobile Image Recognition info:eu-repo/semantics/conferenceObject