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A cross-layer review of deep learning frameworks to ease their optimization and reuse

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10.1109/ISORC49007.2020.00030
 
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hdl:2117/334234

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Tabani, Hamid
Pujol Torramorell, RogerMés informació
Abella Ferrer, JaumeMés informació
Cazorla Almeida, Francisco Javier
Document typeConference report
Defense date2020
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
ProjectCOMPUTACION DE ALTAS PRESTACIONES VII (MINECO-TIN2015-65316-P)
SuPerCom - Sustainable Performance for High-Performance Embedded Computing Systems (EC-H2020-772773)
Abstract
Machine learning and especially Deep Learning (DL) approaches are at the heart of many domains, from computer vision and speech processing to predicting trajectories in autonomous driving and data science. Those approaches mainly build upon Neural Networks (NNs), which are compute-intensive in nature. A plethora of frameworks, libraries and platforms have been deployed for the implementation of those NNs, but end users often lack guidance on what frameworks, platforms and libraries to use to obtain the best implementation for their particular needs. This paper analyzes the DL ecosystem providing a structured view of some of the main frameworks, platforms and libraries for DL implementation. We show how those DL applications build ultimately on some form of linear algebra operations such as matrix multiplication, vector addition, dot product and the like. This analysis allows understanding how optimizations of specific linear algebra functions for specific platforms can be effectively leveraged to maximize specific targets (e.g. performance or power-efficiency) at application level reusing components across frameworks and domains.
Description
©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
CitationTabani, H. [et al.]. A cross-layer review of deep learning frameworks to ease their optimization and reuse. A: IEEE International Symposium on Real-Time Distributed Computing. "2020 IEEE 23rd International Symposium on Real Time Distributed Computing, ISORC 2020: Nashville, Tennessee, USA, 19-21 May 2020: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2020, p. 144-145. ISBN 978-1-7281-6958-3. DOI 10.1109/ISORC49007.2020.00030. 
URIhttp://hdl.handle.net/2117/334234
DOI10.1109/ISORC49007.2020.00030
ISBN978-1-7281-6958-3
Publisher versionhttps://ieeexplore.ieee.org/abstract/document/9112939
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  • Doctorat en Arquitectura de Computadors - Ponències/Comunicacions de congressos [348]
  • Computer Sciences - Ponències/Comunicacions de congressos [622]
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