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Found 6 papers, 2 papers with code
TinyMetaFed: Efficient Federated Meta-Learning for TinyML
The field of Tiny Machine Learning (TinyML) has made substantial advancements in democratizing machine learning on low-footprint devices, such as microcontrollers.
TinyReptile: TinyML with Federated Meta-Learning
Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs).
SeLoC-ML: Semantic Low-Code Engineering for Machine Learning Applications in Industrial IoT
Therefore, this paper presents a framework called Semantic Low-Code Engineering for ML Applications (SeLoC-ML), built on a low-code platform to support the rapid development of ML applications in IIoT by leveraging Semantic Web technologies.
How to Manage Tiny Machine Learning at Scale: An Industrial Perspective
Tiny machine learning (TinyML) has gained widespread popularity where machine learning (ML) is democratized on ubiquitous microcontrollers, processing sensor data everywhere in real-time.
The Synergy of Complex Event Processing and Tiny Machine Learning in Industrial IoT
Focusing on comprehensive networking, big data, and artificial intelligence, the Industrial Internet-of-Things (IIoT) facilitates efficiency and robustness in factory operations.
TinyOL: TinyML with Online-Learning on Microcontrollers
The neural network is first trained using a large amount of pre-collected data on a powerful machine and then flashed to MCUs.
