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Tiny Machine Learning (TinyML): Research Trends and Future Application Opportunities
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DOI：10.1016/j.array.2025.100674.png)
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Abstract
En 中文
Tiny Machine Learning (TinyML) enables artificial intelligence on low-power edge devices, yet a quantitative understanding of TinyML research remains limited. This study addresses this gap through a comprehensive bibliometric analysis of 392 peer-reviewed publications (2020–2024) from the Web of Science, using Biblioshiny and VOSviewer. This article contributes by mapping the first bibliometric structure of TinyML, identifying major trends (exponential publication growth, strong international collaboration, core research themes, key contributors, etc.) and proposing future directions (such as sustainable hardware, federated learning, ethical frameworks, etc.). The findings provide a scholarly foundation and strategic roadmap for advancing scalable, energy-efficient, and privacy-preserving TinyML applications.
Keywords:
Tiny Machine Learning
bibliometric analysis
Web of Science
Biblioshiny
VOSviewer
edge computing
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