# Ariel-ML: Computing Parallelization with Embedded Rust for Neural Networks on Heterogeneous Multi-core Microcontrollers

arXiv: https://arxiv.org/abs/2512.09800 · HTML: https://mlsanity.com/paper/2512.09800/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 46 references that are in the map.
- First posted on arXiv 10 Dec 2025.

## Builds on (most cited first, 11 of 11)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Machine Learning Operations (MLOps): Overview, Definition, and Architecture](https://mlsanity.com/paper/2205.02302/) (2022)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning](https://mlsanity.com/paper/2110.15352/) (2021)
- [TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deployment](https://mlsanity.com/paper/2205.14479/) (2022)
- [Optimizing the Deployment of Tiny Transformers on Low-Power MCUs](https://mlsanity.com/paper/2404.02945/) (2024)
- [TinyML Platforms Benchmarking](https://mlsanity.com/paper/2112.01319/) (2021)
- [U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT](https://mlsanity.com/paper/2306.14574/) (2023)
- [MicroFlow: An Efficient Rust-Based Inference Engine for TinyML](https://mlsanity.com/paper/2409.19432/) (2024)
- [msf-CNN: Patch-based Multi-Stage Fusion with Convolutional Neural Networks for TinyML](https://mlsanity.com/paper/2505.11483/) (2025)
- [DEBUG-HD: Debugging TinyML models on-device using Hyper-Dimensional computing](https://mlsanity.com/paper/2411.10692/) (2024)

## Built on by (most cited first, 1 of 1)

- [PolyChirp: Multi-Species Birdsong Classification Using TinyML on Low-Power Acoustic Sensors](https://mlsanity.com/paper/2608.23101/) (2026)

## Abstract (arXiv)

Low-power microcontroller (MCU) hardware is currently evolving from single-core architectures to predominantly multi-core architectures. In parallel, new embedded software building blocks are more and more written in Rust, while C/C++ dominance fades in this domain. On the other hand, small artificial neural networks (ANN) of various kinds are increasingly deployed in edge AI use cases, thus deployed and executed directly on low-power MCUs. In this context, both incremental improvements and novel innovative services will have to be continuously retrofitted using ANNs execution in software embedded on sensing/actuating systems already deployed in the field. However, there was so far no Rust embedded software platform automating parallelization for inference computation on multi-core MCUs executing arbitrary TinyML models. This paper thus fills this gap by introducing Ariel-ML, a novel toolkit we designed combining a generic TinyML pipeline and an embedded Rust software platform which can take full advantage of multi-core capabilities of various 32bit microcontroller families (Arm Cortex-M, RISC-V, ESP-32). We published the full open source code of its implementation, which we used to benchmark its capabilities using a zoo of various TinyML models. We show that Ariel-ML outperforms prior art in terms of inference latency as expected, and we show that, compared to pre-existing toolkits using embedded C/C++, Ariel-ML achieves comparable memory footprints. Ariel-ML thus provides a useful basis for TinyML practitioners and resource-constrained embedded Rust developers.