mlsanityData to 14 Oct 2026

Intelligence at the Extreme Edge: A Survey on Reformable TinyML

arXiv 2204.00827 ↗First posted 2 Apr 2022cs.LG eess.SPcited by 13

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Abstract (arXiv)

Tiny Machine Learning (TinyML) is an upsurging research field that proposes to democratize the use of Machine Learning and Deep Learning on highly energy-efficient frugal Microcontroller Units. Considering the general assumption that TinyML can only run inference, growing interest in the domain has led to work that makes them reformable, i.e., solutions that permit models to improve once deployed. This work presents a survey on reformable TinyML solutions with the proposal of a novel taxonomy. Here, the suitability of each hierarchical layer for reformability is discussed. Furthermore, we explore the workflow of TinyML and analyze the identified deployment schemes, available tools and the scarcely available benchmarking tools. Finally, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges and future directions.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 24 of its 108 references that are in the map. First posted on arXiv 2 Apr 2022.

Builds on

24 of 108 references.

References · 24

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  3. 2018Continual Lifelong Learning with Neural Networks: A Reviewcited by 1,330
  4. 2019Relational Knowledge Distillationcited by 889
  5. 2020What is the State of Neural Network Pruning?cited by 583
  6. 2020ZeroQ: A Novel Zero Shot Quantization Frameworkcited by 269
  7. 2021Online Continual Learning in Image Classification: An Empirical Surveycited by 245
  8. 2019REMIND Your Neural Network to Prevent Catastrophic Forgettingcited by 197
  9. 2020TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systemscited by 165
  10. 2021MLPerf Tiny Benchmarkcited by 124
  11. 2017Hello Edge: Keyword Spotting on Microcontrollerscited by 110
  12. 2019Latent Replay for Real-Time Continual Learningcited by 97
12 more
  1. 2020Benchmarking TinyML Systems: Challenges and Directioncited by 91
  2. 2021TinyOL: TinyML with Online-Learning on Microcontrollerscited by 65
  3. 2019SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollerscited by 52
  4. 2019Visual Wake Words Datasetcited by 46
  5. 2021A TinyML Platform for On-Device Continual Learning with Quantized Latent Replayscited by 37
  6. 2021Widening Access to Applied Machine Learning with TinyMLcited by 17
  7. 2021TinyML for Ubiquitous Edge AIcited by 13
  8. 2022AI for Next Generation Computing: Emerging Trends and Future Directionscited by 13
  9. 2020Robustifying the Deployment of tinyML Models for Autonomous mini-vehiclescited by 10
  10. 2020A Tiny CNN Architecture for Medical Face Mask Detection for Resource-Constrained Endpointscited by 10
  11. 2021TinyFedTL: Federated Transfer Learning on Tiny Devicescited by 6
  12. 2020Firmware over-the-air programming techniques for IoT networks -- A surveycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 2; 2024: 3; 2025: 5; 2026: 2.

13 papers.

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  1. 2023A Machine Learning-oriented Survey on Tiny Machine Learningcited by 27
  2. 2023TinyML: Tools, Applications, Challenges, and Future Research Directionscited by 14
  3. 2024On-device Online Learning and Semantic Management of TinyML Systemscited by 5
  4. 2024TinySV: Speaker Verification in TinyML with On-device Learningcited by 5
  5. 2025From Tiny Machine Learning to Tiny Deep Learning: A Surveycited by 5
  6. 2025Agentic TinyML for Intent-aware Handover in 6G Wireless Networkscited by 2
  7. 2025Efficient Adaptation of Deep Neural Networks for Semantic Segmentation in Space Applicationscited by 1
  8. 2022A review of TinyMLcited by 0
  9. 2024Accelerating TinyML Inference on Microcontrollers through Approximate Kernelscited by 0
  10. 2025Integration of TinyML and LargeML: A Survey of 6G and Beyondcited by 0
  11. 2025A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Managementcited by 0
  12. 2026What changes after deployment? A survey on On-device Learning in TinyMLcited by 0
1 more
  1. 2026Unifying Data, Memory, and Compute Efficiency in LLM training: A Surveycited by 0