mlsanityData to 7 Oct 2026

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

arXiv 2502.01700 ↗First posted 3 Feb 2025cs.LGcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 15 of its 27 references that are in the map. First posted on arXiv 3 Feb 2025.

Builds on

15 of 27 references.

References · 15

  1. 2020MCUNet: Tiny Deep Learning on IoT Devicescited by 269
  2. 2020TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systemscited by 161
  3. 2022On-Device Training Under 256KB Memorycited by 158
  4. 2021TinyOL: TinyML with Online-Learning on Microcontrollerscited by 65
  5. 2020DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUscited by 57
  6. 2019Memory-Driven Mixed Low Precision Quantization For Enabling Deep Network Inference On Microcontrollerscited by 31
  7. 2022Machine Learning for Microcontroller-Class Hardware: A Reviewcited by 28
  8. 2023A Machine Learning-oriented Survey on Tiny Machine Learningcited by 26
  9. 2022Walle: An End-to-End, General-Purpose, and Large-Scale Production System for Device-Cloud Collaborative Machine Learningcited by 23
  10. 2019FANN-on-MCU: An Open-Source Toolkit for Energy-Efficient Neural Network Inference at the Edge of the Internet of Thingscited by 17
  11. 2021TinyML Platforms Benchmarkingcited by 5
  12. 2022T-RECX: Tiny-Resource Efficient Convolutional neural networks with early-eXitcited by 5
3 more
  1. 2023MLonMCU: TinyML Benchmarking with Fast Retargetingcited by 5
  2. 2022MinUn: Accurate ML Inference on Microcontrollerscited by 3
  3. 2021An Open-Source Tool for Classification Models in Resource-Constrained Hardwarecited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Edge AI Deployment Beyond Models: A BSP-Aware Systems Framework for Industrial Embedded Platformscited by 0
  2. 2026AI Assisted Workflow Optimization and Automationcited by 0