Barrachina, S.; Dolz, MF.; San Juan, P.; Quintana-Ortí, ES. (2022). Efficient and portable GEMM-based convolution operators for deep neural network training on multicore processors. Journal of Parallel and Distributed Computing. 167:240-254. https://doi.org/10.1016/j.jpdc.2022.05.009
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/194790
Title: | Efficient and portable GEMM-based convolution operators for deep neural network training on multicore processors | |
Author: | Barrachina, Sergio Dolz, Manuel F. | |
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[EN] Convolutional Neural Networks (CNNs) play a crucial role in many image recognition and classification tasks, recommender systems, brain-computer interfaces, etc. As a consequence, there is a notable interest in ...[+]
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Copyrigths: | Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) | |
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Publisher version: | https://doi.org/10.1016/j.jpdc.2022.05.009 | |
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This research was funded by Project PID2020-113656RB-C21/C22 supported by MCIN/AEI/10.13039/501100011033 and Prometeo/2019/109 of the Generalitat Valenciana . Manuel F. Dolz was also supported by the Plan Gen-T grant ...[+]
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