From Tiny Machine Learning to Tiny Deep Learning: A Survey
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912706095742976 |
|---|---|
| author | Somvanshi, Shriyank Islam, Md Monzurul Chhetri, Gaurab Chakraborty, Rohit Mimi, Mahmuda Sultana Shuvo, Sawgat Ahmed Islam, Kazi Sifatul Javed, Syed Aaqib Rafat, Sharif Ahmed Dutta, Anandi Das, Subasish |
| author_facet | Somvanshi, Shriyank Islam, Md Monzurul Chhetri, Gaurab Chakraborty, Rohit Mimi, Mahmuda Sultana Shuvo, Sawgat Ahmed Islam, Kazi Sifatul Javed, Syed Aaqib Rafat, Sharif Ahmed Dutta, Anandi Das, Subasish |
| contents | The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). While TinyML initially focused on enabling simple inference tasks on microcontrollers, the emergence of TinyDL marks a paradigm shift toward deploying deep learning models on severely resource-constrained hardware. This survey presents a comprehensive overview of the transition from TinyML to TinyDL, encompassing architectural innovations, hardware platforms, model optimization techniques, and software toolchains. We analyze state-of-the-art methods in quantization, pruning, and neural architecture search (NAS), and examine hardware trends from MCUs to dedicated neural accelerators. Furthermore, we categorize software deployment frameworks, compilers, and AutoML tools enabling practical on-device learning. Applications across domains such as computer vision, audio recognition, healthcare, and industrial monitoring are reviewed to illustrate the real-world impact of TinyDL. Finally, we identify emerging directions including neuromorphic computing, federated TinyDL, edge-native foundation models, and domain-specific co-design approaches. This survey aims to serve as a foundational resource for researchers and practitioners, offering a holistic view of the ecosystem and laying the groundwork for future advancements in edge AI. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18927 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Tiny Machine Learning to Tiny Deep Learning: A Survey Somvanshi, Shriyank Islam, Md Monzurul Chhetri, Gaurab Chakraborty, Rohit Mimi, Mahmuda Sultana Shuvo, Sawgat Ahmed Islam, Kazi Sifatul Javed, Syed Aaqib Rafat, Sharif Ahmed Dutta, Anandi Das, Subasish Machine Learning The rapid growth of edge devices has driven the demand for deploying artificial intelligence (AI) at the edge, giving rise to Tiny Machine Learning (TinyML) and its evolving counterpart, Tiny Deep Learning (TinyDL). While TinyML initially focused on enabling simple inference tasks on microcontrollers, the emergence of TinyDL marks a paradigm shift toward deploying deep learning models on severely resource-constrained hardware. This survey presents a comprehensive overview of the transition from TinyML to TinyDL, encompassing architectural innovations, hardware platforms, model optimization techniques, and software toolchains. We analyze state-of-the-art methods in quantization, pruning, and neural architecture search (NAS), and examine hardware trends from MCUs to dedicated neural accelerators. Furthermore, we categorize software deployment frameworks, compilers, and AutoML tools enabling practical on-device learning. Applications across domains such as computer vision, audio recognition, healthcare, and industrial monitoring are reviewed to illustrate the real-world impact of TinyDL. Finally, we identify emerging directions including neuromorphic computing, federated TinyDL, edge-native foundation models, and domain-specific co-design approaches. This survey aims to serve as a foundational resource for researchers and practitioners, offering a holistic view of the ecosystem and laying the groundwork for future advancements in edge AI. |
| title | From Tiny Machine Learning to Tiny Deep Learning: A Survey |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.18927 |