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Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey

Algoritmi Center, University of Minho, 4800-058 Guimarães, Portugal
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Electronics 2019, 8(11), 1289; https://doi.org/10.3390/electronics8111289
Received: 30 September 2019 / Revised: 28 October 2019 / Accepted: 1 November 2019 / Published: 5 November 2019
(This article belongs to the Section Artificial Intelligence)
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. View Full-Text
Keywords: machine learning; embedded systems; resource-scarce MCUs; FPGA; end-devices machine learning; embedded systems; resource-scarce MCUs; FPGA; end-devices
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Branco, S.; Ferreira, A.G.; Cabral, J. Machine Learning in Resource-Scarce Embedded Systems, FPGAs, and End-Devices: A Survey. Electronics 2019, 8, 1289.

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