# Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers

arXiv: https://arxiv.org/abs/2205.13838 · HTML: https://mlsanity.com/paper/2205.13838/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 27 May 2022.

## Builds on (most cited first, 7 of 7)

- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Conditional Deep Learning for Energy-Efficient and Enhanced Pattern Recognition](https://mlsanity.com/paper/1509.08971/) (2015)
- [UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones](https://mlsanity.com/paper/1611.07688/) (2016)
- [Runtime Configurable Deep Neural Networks for Energy-Accuracy Trade-off](https://mlsanity.com/paper/1607.05418/) (2016)
- [Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With Class-Dependent Confidence](https://mlsanity.com/paper/2204.03431/) (2022)
- [Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization](https://mlsanity.com/paper/1806.11202/) (2018)

## Built on by (most cited first, 3 of 3)

- [Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes](https://mlsanity.com/paper/2306.09789/) (2023)
- [Two-stage Human Activity Recognition on Microcontrollers with Decision Trees and CNNs](https://mlsanity.com/paper/2206.07652/) (2022)
- [Efficient Multi-stage Inference on Tabular Data](https://mlsanity.com/paper/2303.11580/) (2023)

## Abstract (arXiv)

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 necessary only for (few) hard inputs, and wasteful for easier ones. In this work, we propose an early-stopping mechanism for RFs, which terminates the inference as soon as a high-enough classification confidence is reached, reducing the number of weak learners executed for easy inputs. The early-stopping confidence threshold can be controlled at runtime, in order to favor either energy saving or accuracy. We apply our method to three different embedded classification tasks, on a single-core RISC-V microcontroller, achieving an energy reduction from 38% to more than 90% with a drop of less than 0.5% in accuracy. We also show that our approach outperforms previous adaptive ML methods for RFs.