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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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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

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Título: Low precision matrix multiplication for efficient deep learning in NVIDIA Carmel processors
Autor: San Juan-Sebastian, Pablo Rodríguez-Sánchez, Rafael Igual, Francisco D. Alonso-Jordá, Pedro Quintana-Ortí, Enrique S.
Entidad UPV: Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Deep learning , Matrix multiplication , High performance , NVIDIA Carmel system-on-chip (SoC)
Derechos de uso: Reserva de todos los derechos
Fuente:
The Journal of Supercomputing. (issn: 0920-8542 )
DOI: 10.1007/s11227-021-03636-4
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/s11227-021-03636-4
Código del Proyecto:
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/CAM//S2018%2FTCS-4423 /
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/
info:eu-repo/grantAgreement/CAM//PR65%2F19-22445/
info:eu-repo/grantAgreement/GVA//PROMETEO%2F2019%2F109//COMUNICACION Y COMPUTACION INTELIGENTES Y SOCIALES/
Agradecimientos:
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, ...[+]
Tipo: Artículo

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