Random Forests (RFs) are popular Machine Learning models for edge computing, due to their lightweight nature and high accuracy on several common tasks. Large RFs however, still have significant energy costs, a serious concern for battery-operated ultra-low-power devices. Following the adaptive (or dynamic) inference paradigm, we introduce a hardware-friendly early stopping policy for RF-based classifiers, halting the execution as soon as a sufficient prediction confidence is achieved. We benchmark our approach on three state-of-the-art datasets relative to different embedded classification tasks, and deploy our models on a single core RISC-V microcontroller. We achieve an energy reduction ranging from 18% to more than 91%, with an accuracy drop lower than 0.5%. Additionally, we compare our approach with other early-stopping policies, showing that we outperform them.

Low-Overhead Early-Stopping Policies for Efficient Random Forests Inference on Microcontrollers / Daghero, F; Burrello, A; Xie, C; Benini, L; Calimera, A; Macii, E; Poncino, M; Jahier Pagliari, D. - STAMPA. - 661:(2022), pp. 25-47. (Intervento presentato al convegno 29th IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration (VLSI-SoC 2021) tenutosi a Singapore nel October 4-8, 2021) [10.1007/978-3-031-16818-5_2].

Low-Overhead Early-Stopping Policies for Efficient Random Forests Inference on Microcontrollers

Daghero, F;Burrello, A;Xie, C;Benini, L;Calimera, A;Macii, E;Poncino, M;Jahier Pagliari, D
2022

Abstract

Random Forests (RFs) are popular Machine Learning models for edge computing, due to their lightweight nature and high accuracy on several common tasks. Large RFs however, still have significant energy costs, a serious concern for battery-operated ultra-low-power devices. Following the adaptive (or dynamic) inference paradigm, we introduce a hardware-friendly early stopping policy for RF-based classifiers, halting the execution as soon as a sufficient prediction confidence is achieved. We benchmark our approach on three state-of-the-art datasets relative to different embedded classification tasks, and deploy our models on a single core RISC-V microcontroller. We achieve an energy reduction ranging from 18% to more than 91%, with an accuracy drop lower than 0.5%. Additionally, we compare our approach with other early-stopping policies, showing that we outperform them.
2022
978-3-031-16817-8
978-3-031-16818-5
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2974818