article · IEEE Access
This survey explores TinyML, an emerging field of Artificial Intelligence (AI) focused on enabling machine learning on extremely low-profile, resource-constrained devices. It addresses the high energy consumption, carbon footprint, and cost associated with traditional AI algorithms. The research presents a comprehensive literature review of TinyML applications and related efforts, developing a taxonomy of techniques used in diverse areas such as healthcare, smart farming, environmental monitoring, and anomaly detection. The survey also identifies current challenges and suggests future research directions, aiming to stimulate further discussion on TinyML's applications and the integration of resource-constrained devices with edge intelligence.
This research is important because it explores how artificial intelligence can be made more efficient and accessible. By enabling AI on small, low-power devices, TinyML can bring intelligent capabilities to a wider range of applications, reducing environmental impact and operational costs.
TinyML offers a pathway for developing energy-efficient, low-cost AI solutions for various sectors. Potential applications include smart sensors for environmental monitoring, portable health devices, and automated systems in smart farming. This is a foundational survey, indicating early-stage research and development, but it points towards future products and services that leverage resource-constrained edge intelligence.
AI-generated from the published abstract. Always read the original work before citing.
Recent spectacular progress in computational technologies has led to an unprecedented boom in the field of Artificial Intelligence (AI). AI is now used in a plethora of research areas and has demonstrated its capability to bring new approaches and solutions to various research problems. However, the extensive computation required to train AI algorithms comes with a cost. Driven by the need to reduce the energy consumption, the carbon footprint and the cost of computers running machine learning algorithms, TinyML is nowadays considered as a promising AI alternative focusing on technologies and applications for extremely low-profile devices. This paper presents the results of a literature survey of all TinyML applications and related research efforts. Our survey builds a taxonomy of TinyML techniques that have been used so far to bring new solutions to various domains, such as healthcare, smart farming, environment, and anomaly detection. Finally, this survey highlights the remaining challenges and points out possible future research directions. We anticipate that this survey will motivate further discussions on the various fields of applications of TinyML and the synergy of resource-constrained devices and edge intelligence.
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DOI: 10.1109/access.2023.3294111
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