mlsanityData to 7 Oct 2026

MLonMCU: TinyML Benchmarking with Fast Retargeting

arXiv 2306.08951 ↗First posted 15 Jun 2023cs.LGcited by 5

Not on the map. Why?

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 3 of its 10 references that are in the map. First posted on arXiv 15 Jun 2023.

Builds on

3 of 10 references.

References · 3

  1. 2020TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systemscited by 161
  2. 2021MLPerf Tiny Benchmarkcited by 118
  3. 2018CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUscited by 116

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 1.

5 papers.

  1. 2025Benchmarking Energy and Latency in TinyML: A Novel Method for Resource-Constrained AIcited by 4
  2. 2025Energy-Aware Deep Learning on Resource-Constrained Hardwarecited by 3
  3. 2025EdgeMark: An Automation and Benchmarking System for Embedded Artificial Intelligence Toolscited by 2
  4. 2025Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classificationcited by 0
  5. 2026CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systemscited by 0