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Quantitative Analysis of Image Classification Techniques for Memory-Constrained Devices
[article]
2020
arXiv
pre-print
Convolutional Neural Networks, or CNNs, are the state of the art for image classification, but typically come at the cost of a large memory footprint. This limits their usefulness in applications relying on embedded devices, where memory is often a scarce resource. Recently, there has been significant progress in the field of image classification on such memory-constrained devices, with novel contributions like the ProtoNN, Bonsai and FastGRNN algorithms. These have been shown to reach up to
arXiv:2005.04968v4
fatcat:5fnphvrpr5hg5jwjiau7rzdsne