Utilize este identificador para referenciar este registo:
https://hdl.handle.net/1822/62521
Título: | Machine learning in resource-scarce embedded systems, FPGAs, and end-devices: a survey |
Autor(es): | Branco, Sérgio Ferreira, André G. Cabral, Jorge |
Palavras-chave: | machine learning embedded systems resource-scarce MCUs FPGA end-devices |
Data: | 5-Nov-2019 |
Editora: | Multidisciplinary Digital Publishing Institute |
Revista: | Electronics |
Citação: | Branco, S.; Ferreira, A.G.; Cabral, J. Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey. Electronics 2019, 8, 1289. |
Resumo(s): | The number of devices connected to the Internet is increasing, exchanging large amounts of data, and turning the Internet into the 21st-century silk road for data. This road has taken machine learning to new areas of applications. However, machine learning models are not yet seen as complex systems that must run in powerful computers (i.e., Cloud). As technology, techniques, and algorithms advance, these models are implemented into more computational constrained devices. The following paper presents a study about the optimizations, algorithms, and platforms used to implement such models into the network’s end, where highly resource-scarce microcontroller units (MCUs) are found. The paper aims to provide guidelines, taxonomies, concepts, and future directions to help decentralize the network’s intelligence. |
Tipo: | Artigo |
URI: | https://hdl.handle.net/1822/62521 |
DOI: | 10.3390/electronics8111289 |
e-ISSN: | 2079-9292 |
Versão da editora: | https://www.mdpi.com/2079-9292/8/11/1289 |
Arbitragem científica: | yes |
Acesso: | Acesso aberto |
Aparece nas coleções: |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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electronics-08-01289.pdf | 903,51 kB | Adobe PDF | Ver/Abrir |
Este trabalho está licenciado sob uma Licença Creative Commons