# MLonMCU: TinyML Benchmarking with Fast Retargeting

arXiv: https://arxiv.org/abs/2306.08951 · HTML: https://mlsanity.com/paper/2306.08951/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 10 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 3 of 3)

- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [MLPerf Tiny Benchmark](https://mlsanity.com/paper/2106.07597/) (2021)
- [CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs](https://mlsanity.com/paper/1801.06601/) (2018)

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

- [Benchmarking Energy and Latency in TinyML: A Novel Method for Resource-Constrained AI](https://mlsanity.com/paper/2505.15622/) (2025)
- [Energy-Aware Deep Learning on Resource-Constrained Hardware](https://mlsanity.com/paper/2505.12523/) (2025)
- [EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools](https://mlsanity.com/paper/2502.01700/) (2025)
- [Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classification](https://mlsanity.com/paper/2511.02992/) (2025)
- [CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systems](https://mlsanity.com/paper/2606.15004/) (2026)

## Abstract (arXiv)

While there exist many ways to deploy machine learning models on microcontrollers, it is non-trivial to choose the optimal combination of frameworks and targets for a given application. Thus, automating the end-to-end benchmarking flow is of high relevance nowadays. A tool called MLonMCU is proposed in this paper and demonstrated by benchmarking the state-of-the-art TinyML frameworks TFLite for Microcontrollers and TVM effortlessly with a large number of configurations in a low amount of time.