# Dynamic Decision Tree Ensembles for Energy-Efficient Inference on IoT Edge Nodes

arXiv: https://arxiv.org/abs/2306.09789 · HTML: https://mlsanity.com/paper/2306.09789/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 49 references that are in the map.
- First posted on arXiv 16 Jun 2023.

## Builds on (most cited first, 14 of 14)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Slimmable Neural Networks](https://mlsanity.com/paper/1812.08928/) (2018)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs](https://mlsanity.com/paper/2008.07127/) (2020)
- [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)
- [Ultra-compact Binary Neural Networks for Human Activity Recognition on RISC-V Processors](https://mlsanity.com/paper/2205.12781/) (2022)
- [Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks](https://mlsanity.com/paper/2209.00839/) (2022)
- [Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing](https://mlsanity.com/paper/2004.06896/) (2020)
- [DNN is not all you need: Parallelizing Non-Neural ML Algorithms on Ultra-Low-Power IoT Processors](https://mlsanity.com/paper/2107.09448/) (2021)
- [Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers](https://mlsanity.com/paper/2205.13838/) (2022)

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

- [PACE: Prune-And-Compress Ensemble Models](https://mlsanity.com/paper/2605.06278/) (2026)
- [InTreeger: An End-to-End Framework for Integer-Only Decision Tree Inference](https://mlsanity.com/paper/2505.15391/) (2025)
- [RETENTION: Resource-Efficient Tree-Based Ensemble Model Acceleration with Content-Addressable Memory](https://mlsanity.com/paper/2506.05994/) (2025)

## Abstract (arXiv)

With the increasing popularity of Internet of Things (IoT) devices, there is a growing need for energy-efficient Machine Learning (ML) models that can run on constrained edge nodes. Decision tree ensembles, such as Random Forests (RFs) and Gradient Boosting (GBTs), are particularly suited for this task, given their relatively low complexity compared to other alternatives. However, their inference time and energy costs are still significant for edge hardware. Given that said costs grow linearly with the ensemble size, this paper proposes the use of dynamic ensembles, that adjust the number of executed trees based both on a latency/energy target and on the complexity of the processed input, to trade-off computational cost and accuracy. We focus on deploying these algorithms on multi-core low-power IoT devices, designing a tool that automatically converts a Python ensemble into optimized C code, and exploring several optimizations that account for the available parallelism and memory hierarchy. We extensively benchmark both static and dynamic RFs and GBTs on three state-of-the-art IoT-relevant datasets, using an 8-core ultra-lowpower System-on-Chip (SoC), GAP8, as the target platform. Thanks to the proposed early-stopping mechanisms, we achieve an energy reduction of up to 37.9% with respect to static GBTs (8.82 uJ vs 14.20 uJ per inference) and 41.7% with respect to static RFs (2.86 uJ vs 4.90 uJ per inference), without losing accuracy compared to the static model.