# EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Tools

arXiv: https://arxiv.org/abs/2502.01700 · HTML: https://mlsanity.com/paper/2502.01700/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 27 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 15 of 15)

- [MCUNet: Tiny Deep Learning on IoT Devices](https://mlsanity.com/paper/2007.10319/) (2020)
- [TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems](https://mlsanity.com/paper/2010.08678/) (2020)
- [On-Device Training Under 256KB Memory](https://mlsanity.com/paper/2206.15472/) (2022)
- [TinyOL: TinyML with Online-Learning on Microcontrollers](https://mlsanity.com/paper/2103.08295/) (2021)
- [DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs](https://mlsanity.com/paper/2008.07127/) (2020)
- [Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollers](https://mlsanity.com/paper/1905.13082/) (2019)
- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [A Machine Learning-oriented Survey on Tiny Machine Learning](https://mlsanity.com/paper/2309.11932/) (2023)
- [Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learning](https://mlsanity.com/paper/2205.14833/) (2022)
- [FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Things](https://mlsanity.com/paper/1911.03314/) (2019)
- [TinyML Platforms Benchmarking](https://mlsanity.com/paper/2112.01319/) (2021)
- [T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXit](https://mlsanity.com/paper/2207.06613/) (2022)
- [MLonMCU: TinyML Benchmarking with Fast Retargeting](https://mlsanity.com/paper/2306.08951/) (2023)
- [MinUn: Accurate ML Inference on Microcontrollers](https://mlsanity.com/paper/2210.16556/) (2022)
- [An Open-Source Tool for Classification Models in Resource-Constrained Hardware](https://mlsanity.com/paper/2105.05983/) (2021)

## Built on by (most cited first, 2 of 2)

- [Edge AI Deployment Beyond Models: A BSP-Aware Systems Framework for Industrial Embedded Platforms](https://mlsanity.com/paper/2605.26119/) (2026)
- [AI Assisted Workflow Optimization and Automation](https://mlsanity.com/paper/2609.14323/) (2026)

## Abstract (arXiv)

The integration of artificial intelligence (AI) into embedded devices, a paradigm known as embedded artificial intelligence (eAI) or tiny machine learning (TinyML), is transforming industries by enabling intelligent data processing at the edge. However, the many tools available in this domain leave researchers and developers wondering which one is best suited to their needs. This paper provides a review of existing eAI tools, highlighting their features, trade-offs, and limitations. Additionally, we introduce EdgeMark, an open-source automation system designed to streamline the workflow for deploying and benchmarking machine learning (ML) models on embedded platforms. EdgeMark simplifies model generation, optimization, conversion, and deployment while promoting modularity, reproducibility, and scalability. Experimental benchmarking results showcase the performance of widely used eAI tools, including TensorFlow Lite Micro (TFLM), Edge Impulse, Ekkono, and Renesas eAI Translator, across a wide range of models, revealing insights into their relative strengths and weaknesses. The findings provide guidance for researchers and developers in selecting the most suitable tools for specific application requirements, while EdgeMark lowers the barriers to adoption of eAI technologies.