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A Machine Learning-Oriented Survey on Tiny Machine Learning
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DOI：10.1109/ACCESS.2024.3365349.png)
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摘要
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The emergence of Tiny Machine Learning (TinyML) has positively revolutionized the field of Artificial Intelligence by promoting the joint design of resource-constrained IoT hardware devices and their learning-based software architectures. TinyML carries an essential role within the fourth and fifth industrial revolutions in helping societies, economies, and individuals employ effective AI-infused computing technologies (e.g., smart cities, automotive, and medical robotics). Given its multidisciplinary nature, the field of TinyML has been approached from many different angles: this comprehensive survey wishes to provide an up-to-date overview focused on all the learning algorithms within TinyML-based solutions. The survey is based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodological flow, allowing for a systematic and complete literature survey. In particular, firstly, we will examine the three different workflows for implementing a TinyML-based system, i.e., ML-oriented, HW-oriented, and co-design. Secondly, we propose a taxonomy that covers the learning panorama under the TinyML lens, examining in detail the different families of model optimization and design, as well as the state-of-the-art learning techniques. Thirdly, this survey will present the distinct features of hardware devices and software tools that represent the current state-of-the-art for TinyML intelligent edge applications. Finally, we discuss the challenges and future directions.
Keyword:
TinyML
edge intelligence
efficient deep learning
embedded systems
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review基于FPGA的用于学习和分类的深度学习网络加速器: 综述.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
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