# U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT

arXiv: https://arxiv.org/abs/2306.14574 · HTML: https://mlsanity.com/paper/2306.14574/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 26 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [ML-EXray: Visibility into ML Deployment on the Edge](https://mlsanity.com/paper/2111.04779/) (2021)
- [TinyML Platforms Benchmarking](https://mlsanity.com/paper/2112.01319/) (2021)

## Built on by (most cited first, 5 of 5)

- [TinyChirp: Bird Song Recognition Using TinyML Models on Low-power Wireless Acoustic Sensors](https://mlsanity.com/paper/2407.21453/) (2024)
- [DEBUG-HD: Debugging TinyML models on-device using Hyper-Dimensional computing](https://mlsanity.com/paper/2411.10692/) (2024)
- [Ariel-ML: Computing Parallelization with Embedded Rust for Neural Networks on Heterogeneous Multi-core Microcontrollers](https://mlsanity.com/paper/2512.09800/) (2025)
- [PolyChirp: Multi-Species Birdsong Classification Using TinyML on Low-Power Acoustic Sensors](https://mlsanity.com/paper/2608.23101/) (2026)
- [Towards a Cloud Fog Edge System for Smart Building](https://mlsanity.com/paper/2610.01647/) (2026)

## Abstract (arXiv)

Results from the TinyML community demonstrate that, it is possible to execute machine learning models directly on the terminals themselves, even if these are small microcontroller-based devices. However, to date, practitioners in the domain lack convenient all-in-one toolkits to help them evaluate the feasibility of executing arbitrary models on arbitrary low-power IoT hardware. To this effect, we present in this paper U-TOE, a universal toolkit we designed to facilitate the task of IoT designers and researchers, by combining functionalities from a low-power embedded OS, a generic model transpiler and compiler, an integrated performance measurement module, and an open-access remote IoT testbed. We provide an open source implementation of U-TOE and we demonstrate its use to experimentally evaluate the performance of various models, on a wide variety of low-power IoT boards, based on popular microcontroller architectures. U-TOE allows easily reproducible and customizable comparative evaluation experiments on a wide variety of IoT hardware all-at-once. The availability of a toolkit such as U-TOE is desirable to accelerate research combining Artificial Intelligence and IoT towards fully exploiting the potential of edge computing.