Tiny Machine Learning and On-Device Inference: A Survey of Applications, Challenges, and Future Directions
The growth in artificial intelligence and its applications has led to increased data processing and inference requirements. Traditional cloud-based inference solutions are often used but may prove inadequate for applications requiring near-instantaneous response times. This review examines Tiny Mach...
Saved in:
| Main Authors: | Soroush Heydari, Qusay H. Mahmoud |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/10/3191 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Optimising TinyML with quantization and distillation of transformer and mamba models for indoor localisation on edge devices
by: Thanaphon Suwannaphong, et al.
Published: (2025-03-01) -
Microcontroller-Based EdgeML: Health Monitoring for Stress and Sleep via HRV
by: Priyanshu Srivastava, et al.
Published: (2024-12-01) -
Engineering a multi model fallback system for edge devices
by: Gaurav Kadve, et al.
Published: (2025-06-01) -
Advancing TinyML in IoT: A Holistic System-Level Perspective for Resource-Constrained AI
by: Leandro Antonio Pazmiño Ortiz, et al.
Published: (2025-06-01) -
Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications
by: Moez Hizem, et al.
Published: (2025-04-01)