AI at the Edge: Trends and Innovations in Tiny Machine Learning Models for IoT and Embedded Systems in Synergy with Neuton.AI

Trajkovska, Aneta and Markoski, Aleksandar (2024) AI at the Edge: Trends and Innovations in Tiny Machine Learning Models for IoT and Embedded Systems in Synergy with Neuton.AI. Proceedings of the 14th International Conference on Applied Internet and Information Technologies AIIT 2024. pp. 304-311. ISSN ISBN 978-86-7672-379-9

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Abstract

The trajectory of technological evolution is increasingly oriented towards the development of intelligent solutions that enhance both the efficiency and functionality of everyday life. As technological advancements accelerate, we are witnessing a paradigm shift in the execution of technical processes, aimed at simplifying device interactions while simultaneously enhancing control and automation. The rise of AI at the edge is revolutionizing the way we approach machine learning in the context of IoT and embedded systems. Edge AI, which brings the power of machine learning to edge devices, allows for real-time data processing and decision making,
enabling devices to operate independently of cloud-based systems. This innovation is
crucial for applications requiring low-latency responses, such as autonomous vehicles, smart cities, and industrial automation. The convergence of AI, IoT, and edge computing is thus driving significant innovation in embedded systems, with trends indicating a growing emphasis on lightweight machine learning models, energy-efficient algorithms, and scalable architectures. In this paper, we will conduct an in-depth exploration of the utilization of TinyML systems, focusing particularly on practical case studies and best practices associated with neuton.ai. By examining practical use cases of neuton.ai, we will highlight its contributions to advancing the field, including innovations in model optimization, scalability, and real-world deployment strategies.

Item Type:	Article
Subjects:	Scientific Fields (Frascati) > Engineering and Technology > Electrical engineering, electronic engineering,information engineering
Divisions:	Faculty of Information and Communication Technologies
Depositing User:	Prof. d-r. Aleksandar Markoski
Date Deposited:	15 Jan 2025 20:39
Last Modified:	15 Jan 2025 20:39
URI:	https://eprints.uklo.edu.mk/id/eprint/10552

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