# TinyML Platforms Benchmarking

arXiv: https://arxiv.org/abs/2112.01319 · HTML: https://mlsanity.com/paper/2112.01319/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 30 Nov 2021.

## Builds on (most cited first, 10 of 10)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Quantizing deep convolutional networks for efficient inference: A whitepaper](https://mlsanity.com/paper/1806.08342/) (2018)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [Benchmarking TinyML Systems: Challenges and Direction](https://mlsanity.com/paper/2003.04821/) (2020)
- [Machine Learning at the Network Edge: A Survey](https://mlsanity.com/paper/1908.00080/) (2019)
- [TinyML for Ubiquitous Edge AI](https://mlsanity.com/paper/2102.01255/) (2021)
- [TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition with Short Range Radars](https://mlsanity.com/paper/2006.16281/) (2020)
- [Measuring what Really Matters: Optimizing Neural Networks for TinyML](https://mlsanity.com/paper/2104.10645/) (2021)

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

- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [U-TOE: Universal TinyML On-board Evaluation Toolkit for Low-Power IoT](https://mlsanity.com/paper/2306.14574/) (2023)
- [EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools](https://mlsanity.com/paper/2502.01700/) (2025)
- [Tiny-HR: Towards an interpretable machine learning pipeline for heart rate estimation on edge devices](https://mlsanity.com/paper/2208.07981/) (2022)
- [Ariel-ML: Computing Parallelization with Embedded Rust for Neural Networks on Heterogeneous Multi-core Microcontrollers](https://mlsanity.com/paper/2512.09800/) (2025)

## Abstract (arXiv)

Recent advances in state-of-the-art ultra-low power embedded devices for machine learning (ML) have permitted a new class of products whose key features enable ML capabilities on microcontrollers with less than 1 mW power consumption (TinyML). TinyML provides a unique solution by aggregating and analyzing data at the edge on low-power embedded devices. However, we have only recently been able to run ML on microcontrollers, and the field is still in its infancy, which means that hardware, software, and research are changing extremely rapidly. Consequently, many TinyML frameworks have been developed for different platforms to facilitate the deployment of ML models and standardize the process. Therefore, in this paper, we focus on bench-marking two popular frameworks: Tensorflow Lite Micro (TFLM) on the Arduino Nano BLE and CUBE AI on the STM32-NucleoF401RE to provide a standardized framework selection criterion for specific applications.