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Optimum Network/Framework Selection from High-Level Specifications in Embedd

AuthorsVelasco-Montero, Delia; Fernández-Berni, J. CSIC ORCID CVN; Carmona-Galán, R. CSIC ORCID ; Rodríguez-Vázquez, Ángel CSIC ORCID
KeywordsDeep Learning
Convolutional neural networks
Embedded Vision
High-Level Specifications
Issue DateOct-2018
CitationLecture Notes in Computational Science and Engineering, 11182: 369-379 (2018)
AbstractThis paper benchmarks 16 combinations of popular Deep Neural Networks for 1000-category image recognition and Deep Learn- ing frameworks on an embedded platform. A Figure of Merit based on high-level specifications is introduced. By sweeping the relative weight of accuracy, throughput and power consumption on global performance, we demonstrate that only a reduced set of the analyzed combinations must actually be considered for real deployment. We also report the op- timum network/framework selection for all possible application scenarios de ned in those terms, i.e. weighted balance of the aforementioned pa- rameters. Our approach can be extended to other networks, frameworks and performance parameters, thus supporting system-level design deci- sions in the ever-changing ecosystem of Deep Learning technology
Descriptionin Advanced Concepts for Intelligent Vision Systems (ACIVS), Poitiers, France, September 2018, ISBN 978-3-030-01448-3,
Appears in Collections:(IMSE-CNM) Artículos
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