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High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS

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High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS

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Castelló, A.; SERGIO BARRACHINA; Dolz Zaragozá, MF.; Enrique S. Quintana-Ortí; San Juan-Sebastian, P.; Tomás Domínguez, AE. (2022). High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS. Journal of Systems Architecture. 125:1-9. https://doi.org/10.1016/j.sysarc.2022.102459

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/197569

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Title: High performance and energy efficient inference for deep learning on multicore ARM processors using general optimization techniques and BLIS
Author: Castelló, Adrián SERGIO BARRACHINA DOLZ ZARAGOZÁ, MANUEL FRANCISCO Enrique S. Quintana-Ortí San Juan-Sebastian, Pablo Tomás Domínguez, Andrés Enrique
UPV Unit: Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors
Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Issued date:
Abstract:
[EN] We evolve PyDTNN, a framework for distributed parallel training of Deep Neural Networks (DNNs), into an efficient inference tool for convolutional neural networks. Our optimization process on multicore ARM processors ...[+]
Subjects: Convolutional neural network , Inference , Multicore low-power processors
Copyrigths: Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
Source:
Journal of Systems Architecture. (issn: 1383-7621 )
DOI: 10.1016/j.sysarc.2022.102459
Publisher:
Elsevier
Publisher version: https://doi.org/10.1016/j.sysarc.2022.102459
Project ID:
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-82972-R/ES/TECNICAS ALGORITMICAS PARA COMPUTACION DE ALTO RENDIMIENTO CONSCIENTE DEL CONSUMO ENERGETICO Y RESISTENTE A ERRORES/
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F109//COMUNICACION Y COMPUTACION INTELIGENTES Y SOCIALES/
info:eu-repo/grantAgreement/GVA//CDEIGENT%2F2018%2F014//Plan GenT/
Thanks:
This research was partially sponsored by projects TIN2017-82972-R of Ministerio de Ciencia, Innovacion y Universidades, Spain and Prometeo/2019/109 of the Generalitat Valenciana, Spain. Adrian Castello was supported by the ...[+]
Type: Artículo

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