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Low precision matrix multiplication for efficient deep learning in NVIDIA Carmel processors

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Low precision matrix multiplication for efficient deep learning in NVIDIA Carmel processors

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dc.contributor.author San Juan-Sebastian, Pablo es_ES
dc.contributor.author Rodríguez-Sánchez, Rafael es_ES
dc.contributor.author Igual, Francisco D. es_ES
dc.contributor.author Alonso-Jordá, Pedro es_ES
dc.contributor.author Quintana-Ortí, Enrique S. es_ES
dc.date.accessioned 2022-11-10T19:02:43Z
dc.date.available 2022-11-10T19:02:43Z
dc.date.issued 2021-10 es_ES
dc.identifier.issn 0920-8542 es_ES
dc.identifier.uri http://hdl.handle.net/10251/189610
dc.description.abstract [EN] We introduce a high performance, multi-threaded realization of the gemm kernel for the ARMv8.2 architecture that operates with 16-bit (half precision)/queryKindly check and confirm whether the corresponding author is correctly identified. floating point operands. Our code is especially designed for efficient machine learning inference (and to a certain extent, also training) with deep neural networks. The results on the NVIDIA Carmel multicore processor, which implements the ARMv8.2 architecture, show considerable performance gains for the gemm kernel, close to the theoretical peak acceleration that could be expected when moving from 32-bit arithmetic/data to 16-bit. Combined with the type of convolution operator arising in convolutional neural networks, the speed-ups are more modest though still relevant. es_ES
dc.description.sponsorship This work was supported by projects TIN2017-82972-R and RTI2018-093684-B-I00 from the Ministerio de Ciencia, Innovacion y Universidades, project S2018/TCS-4423 of the Comunidad de Madrid, project PR65/19-22445 of the UCM, and project Prometeo/2019/109 of the Generalitat Valenciana. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof The Journal of Supercomputing es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Deep learning es_ES
dc.subject Matrix multiplication es_ES
dc.subject High performance es_ES
dc.subject NVIDIA Carmel system-on-chip (SoC) es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Low precision matrix multiplication for efficient deep learning in NVIDIA Carmel processors es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s11227-021-03636-4 es_ES
dc.relation.projectID 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/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CAM//S2018%2FTCS-4423 / es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-093684-B-I00/ES/HETEROGENEIDAD Y ESPECIALIZACION EN LA ERA POST-MOORE/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CAM//PR65%2F19-22445/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F109//COMUNICACION Y COMPUTACION INTELIGENTES Y SOCIALES/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica es_ES
dc.description.bibliographicCitation San Juan-Sebastian, P.; Rodríguez-Sánchez, R.; Igual, FD.; Alonso-Jordá, P.; Quintana-Ortí, ES. (2021). Low precision matrix multiplication for efficient deep learning in NVIDIA Carmel processors. The Journal of Supercomputing. 77(10):11257-11269. https://doi.org/10.1007/s11227-021-03636-4 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s11227-021-03636-4 es_ES
dc.description.upvformatpinicio 11257 es_ES
dc.description.upvformatpfin 11269 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 77 es_ES
dc.description.issue 10 es_ES
dc.relation.pasarela S\448133 es_ES
dc.contributor.funder Comunidad de Madrid es_ES
dc.contributor.funder Generalitat Valenciana es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
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