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The not-so-easy task of taking heavy-lift ML models to the ..:
, In:
Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing
,
Pereira, Lucas Caetano
;
Guterres, Bruna
;
Sbrissa, Kauê
... - p. 699-706 , 2023
Link:
https://dl.acm.org/doi/10.1145/3555776.3577742
RT T1 <cTitle>Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing</cTitle>: T1 <Title>The not-so-easy task of taking heavy-lift ML models to the edge: a performance-watt perspective</Title> UL https://suche.suub.uni-bremen.de/peid=acm-3577742&Exemplar=1&LAN=DE A1 Pereira, Lucas Caetano A1 Guterres, Bruna A1 Sbrissa, Kauê A1 Mendes, Amanda A1 Vermeulen, Francisca A1 Lain, Lisl A1 Smith, Marié A1 Martinez, Javier A1 Drews, Paulo A1 Duarte, Nelson A1 Oliveira, Vinicus A1 Botelho, Silvia A1 Pias, Marcelo PB ACM YR 2023 K1 edge computing K1 artificial intelligence K1 edge-based deep learning K1 performance and energy efficiency K1 Computer systems organization K1 Embedded and cyber-physical systems K1 Embedded systems K1 Applied computing K1 Computers in other domains SP 699 OP 706 LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3555776.3577742 DO https://dl.acm.org/doi/10.1145/3555776.3577742 SF ELIB - SuUB Bremen
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