| Abadade, Y. [1] |
2023 |
This survey provides a comprehensive analysis of TinyML, its architecture, and its use cases. |
The study identifies current issues in TinyML but does not suggest how to solve them. |
| Capogrosso, L. [2] |
2024 |
The survey describes all learning algorithms used in TinyML implementations. |
The study insufficiently covers TinyML’s use cases. |
| Elhanashi, A. [3] |
2024 |
The survey discusses TinyML’s use in various applications. |
Their analysis does not provide a sufficient explanation of TinyML’s performance. |
| Tsoukas, V. [4] |
2024 |
This survey classifies TinyML optimization techniques and reviews their applications. |
The survey explains ML techniques and their applications in detail but offers limited breadth beyond that. |
| Liu, S. [5] |
2024 |
This study explains TinyML edge inference and compares latency differences of edge vs. cloud inference. |
More focused on edge inference rather than TinyML. |
| Ren, H. [6] |
2022 |
This study provides a schema for managing TinyML models in IoT devices for industrial settings. |
The study limits itself to industrial settings only. |
