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- W2985361718 abstract "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." @default.
- W2985361718 created "2019-11-22" @default.
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- W2985361718 date "2019-11-05" @default.
- W2985361718 modified "2023-10-01" @default.
- W2985361718 title "Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey" @default.
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- W2985361718 doi "https://doi.org/10.3390/electronics8111289" @default.
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