Embedded Artificial Intelligence for IoT Applications Using the MAX78000
Uloženo v:
| Název: | Embedded Artificial Intelligence for IoT Applications Using the MAX78000 |
|---|---|
| Autoři: | Balbi Antunes, Martina Maria, Doherty, Lance, Watteyne, Thomas |
| Přispěvatelé: | BALBI, Martina |
| Zdroj: | IEEE Access, Vol 13, Pp 38979-39005 (2025) |
| Informace o vydavateli: | Institute of Electrical and Electronics Engineers (IEEE), 2025. |
| Rok vydání: | 2025 |
| Témata: | [INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI], Internet of things, Artificial intelligence, Internet of Things, Convolutional neural network, [INFO] Computer Science [cs], Artificial intellgence, hardware accelerators, [INFO.INFO-ES] Computer Science [cs]/Embedded Systems, TK1-9971, convolutional neural networks, embedded systems, Electrical engineering. Electronics. Nuclear engineering, Hardware accelerator, Embedded system |
| Popis: | Recent advances in embedded AI and IoT have revolutionized the development of intelligent edge devices. This work provides a tutorial on developing and deploying AI models on the MAX78000, a low-power microcontroller designed specifically for AI applications. Starting with the foundational understanding of neural networks and machine learning, this tutorial explores the architecture and capabilities of the MAX78000, which integrates a CNN accelerator with an ARM Cortex-M4 core. We give practical guidance on creating, training, and quantizing AI models, detailing essential tools, frameworks, and the deployment process. Real-world examples illustrate the versatility of AI microcontrollers and their performance in various IoT applications. Emphasizing the importance of accessible development tools, this tutorial aims to increase awareness within the IoT community about low-power accelerators. This enables developers to create efficient, real-time AI solutions, highlighting the transformative potential of embedded AI in IoT. |
| Druh dokumentu: | Article |
| Popis souboru: | application/pdf |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/access.2025.3546557 |
| Přístupová URL adresa: | https://doaj.org/article/0b1e99f6485f4d21a609b2bcaaf40462 https://inria.hal.science/hal-04982025v1 https://doi.org/10.1109/access.2025.3546557 https://inria.hal.science/hal-04982025v1/document |
| Rights: | CC BY |
| Přístupové číslo: | edsair.doi.dedup.....2e46208a7b6c05f5844fcda3b2944c24 |
| Databáze: | OpenAIRE |
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstrakt: | Recent advances in embedded AI and IoT have revolutionized the development of intelligent edge devices. This work provides a tutorial on developing and deploying AI models on the MAX78000, a low-power microcontroller designed specifically for AI applications. Starting with the foundational understanding of neural networks and machine learning, this tutorial explores the architecture and capabilities of the MAX78000, which integrates a CNN accelerator with an ARM Cortex-M4 core. We give practical guidance on creating, training, and quantizing AI models, detailing essential tools, frameworks, and the deployment process. Real-world examples illustrate the versatility of AI microcontrollers and their performance in various IoT applications. Emphasizing the importance of accessible development tools, this tutorial aims to increase awareness within the IoT community about low-power accelerators. This enables developers to create efficient, real-time AI solutions, highlighting the transformative potential of embedded AI in IoT. |
|---|---|
| ISSN: | 21693536 |
| DOI: | 10.1109/access.2025.3546557 |