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Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers. (arXiv:2205.13838v1 [cs.LG])
May 30, 2022, 1:10 a.m. | Francesco Daghero, Alessio Burrello, Chen Xie, Luca Benini, Andrea Calimera, Enrico Macii, Massimo Poncino, Daniele Jahier Pagliari
cs.LG updates on arXiv.org arxiv.org
Random Forests (RFs) are widely used Machine Learning models in low-power
embedded devices, due to their hardware friendly operation and high accuracy on
practically relevant tasks. The accuracy of a RF often increases with the
number of internal weak learners (decision trees), but at the cost of a
proportional increase in inference latency and energy consumption. Such costs
can be mitigated considering that, in most applications, inputs are not all
equally difficult to classify. Therefore, a large RF is often …
arxiv energy inference microcontrollers random random forests
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