A Memory Representation of Random Forests Optimized for Resource-Limited Embedded Devices
Bibliographic record
Abstract
Random forests are a versatile and effective machine learning technique widely applied across various tasks. With the increasing demand for deploying machine learning models on resource-constrained embedded devices, such as microcontrollers, challenges arise from the growing complexity of modern datasets. These challenges often result in models that are too large in memory and storage requirements to be feasibly implemented on small devices. In this work, we propose a lossless memory representation of random forests that significantly limits the amount of random-access memory (RAM) required for prediction tasks, while also reducing the amount of non-volatile memory needed to store the model. The approach achieves efficiency by embedding the data of leaf nodes within the decision nodes, thereby streamlining the tree structure. Additionnally, it supports in-place prediction without requiring a decompression step. To evaluate our method, we implemented four random forests derived from real-world datasets onto four microcontroller platforms. Our results demonstrate that prediction tasks can be performed using at most 144 bytes of RAM for classification tasks, and at most 48 bytes for regression tasks, while memory accesses account for a maximum of 27.0% of the total CPU cycles. On the fastest platform, prediction times ranged between 59 and 75 μs, highlighting the suitability of this method for a variety of real-time applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".