# Field of Groves: An Energy-Efficient Random Forest

## TL;DR

The paper introduces Field of Groves (FoG), a novel energy-efficient random forest implementation that maintains high accuracy while significantly reducing energy consumption in mobile and embedded systems.

## Contribution

FoG is a new random forest approach that achieves CNN-competitive accuracy with much lower energy use in energy-constrained environments.

## Key findings

- FoG consumes 1.48x to 34.7x less energy than other ML models at similar accuracy.
- FoG achieves 18% higher accuracy than SVM_LR on average.
- FoG significantly reduces energy consumption in mobile and embedded systems.

## Abstract

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.