Advances in Tiny Machine Learning (TinyML): Applications, Models, and Implementation

Dear Colleagues,

Tiny machine learning (TinyML) represents a paradigm shift in the field of machine learning, where models are deployed directly onto ultra-low-power, resource-constrained devices such as microcontrollers and edge sensors. Unlike traditional machine learning approaches that rely on centralized processing power, TinyML leverages the capabilities of edge devices to perform inference tasks locally, enabling real-time decision-making and autonomous functionality without constant reliance on cloud connectivity. This Special Issue, “Advances in Tiny Machine Learning (TinyML): Applications, Models, and Implementation”, focuses on exploring this burgeoning field with the aim of elucidating the latest advancements, challenges, and applications within this domain.

The topics covered in this Special Issue include, but are not limited to:

• Innovative applications and use cases across various domains;
• Hardware architecture optimization;
• Interpreters and code generator frameworks;
• Model compression;
• Efficient training methods;
• Federated learning approaches;
• Energy-efficient inference methods;
• Practical deployment strategies for TinyML devices;
• Efficient communication protocols for TinyML;
• Edge computing in TinyML applications;
• Real-time processing in TinyML;
• Integration of 5G/6G in TinyML applications;
• Case studies of TinyML applications.

This Special Issue aims to serve as a platform for researchers, engineers, and practitioners to disseminate their cutting-edge research findings, exchange ideas, and foster collaborations in the field of TinyML. By showcasing state-of-the-art methodologies, practical implementations, and emerging trends, it endeavours to advance the understanding and adoption of TinyML, thereby catalysing its integration into diverse application domains.

Dr. Giovanni Delnevo
Prof. Dr. Pietro Manzoni
Guest Editors

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