Field of Groves: An Energy-Efficient Random Forest
This addresses energy efficiency for machine learning in mobile and embedded systems, representing an incremental improvement over existing methods.
The paper tackles the problem of machine learning algorithms losing accuracy under tight energy budgets in mobile and embedded systems by presenting a field of groves implementation of random forests, which achieves comparable accuracy to CNNs and SVMs while consuming significantly lower energy per classification, e.g., ~34.7x lower than CNN.
Machine Learning (ML) algorithms, like Convolutional Neural Networks (CNN), Support Vector Machines (SVM), etc. have become widespread and can achieve high statistical performance. However their accuracy decreases significantly in energy-constrained mobile and embedded systems space, where all computations need to be completed under a tight energy budget. In this work, we present a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to CNNs and SVMs under tight energy budgets. Evaluation of the FoG shows that at comparable accuracy it consumes ~1.48x, ~24x, ~2.5x, and ~34.7x lower energy per classification compared to conventional RF, SVM_RBF , MLP, and CNN, respectively. FoG is ~6.5x less energy efficient than SVM_LR, but achieves 18% higher accuracy on average across all considered datasets.