A review on TinyML: State-of-the-art and prospects
Journal of King Saud University - Computer and Information Sciences, 2022 Machine learning has become an indispensable part of the existing technological domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to imply machine learning techniques at the resource constrained embedded devices at ...
Partha Pratim Ray
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A Comprehensive Survey on TinyML
IEEE Access, 2023 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 ...
Youssef Abadade +5 more
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TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review
Future Internet, 2022 The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within ...
Spyros Sioutas +2 more
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TinyML in Industrial IoT: A Systematic Review of Applications, System Components, and Methodologies [PDF]
SensorsTiny Machine Learning (TinyML) enables Machine Learning (ML) models to run on resource-constrained devices, which is critical for Industrial Internet of Things (IIoT) systems requiring low latency, energy efficiency, and local decision-making ...
Shahad Alharthi +2 more
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Tiny Machine Learning and On-Device Inference: A Survey of Applications, Challenges, and Future Directions [PDF]
SensorsThe growth in artificial intelligence and its applications has led to increased data processing and inference requirements. Traditional cloud-based inference solutions are often used but may prove inadequate for applications requiring near-instantaneous ...
Soroush Heydari, Qusay H. Mahmoud
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Sustainable E-Health: Energy-Efficient Tiny AI for Epileptic Seizure Detection via EEG [PDF]
Biomedical Engineering and Computational BiologyTiny Artificial Intelligence (Tiny AI) is transforming resource-constrained embedded systems, particularly in e-health applications, by introducing a shift in Tiny Machine Learning (TinyML) and its integration with the Internet of Things (IoT).
Moez Hizem +4 more
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Empowering voice assistants with TinyML for user-centric innovations and real-world applications [PDF]
Scientific ReportsThis study explores the motivations behind integrating TinyML-based voice assistants into daily life, focusing on enhancing their user interface (UI) and functionality to improve user experience. This research discusses real-world applications like smart
Sireesha Chittepu +2 more
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Reliable ECG Anomaly Detection on Edge Devices for Internet of Medical Things Applications [PDF]
SensorsThe advent of Tiny Machine Learning (TinyML) has unlocked the potential to deploy machine learning models on resource-constrained edge devices, revolutionizing real-time monitoring in Internet of Medical Things (IoMT) applications.
Moez Hizem +4 more
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Micromachines, 2022 Recently, the Internet of Things (IoT) has gained a lot of attention, since IoT devices are placed in various fields. Many of these devices are based on machine learning (ML) models, which render them intelligent and able to make decisions.
Norah N. Alajlan, Dina M. Ibrahim
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Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices
Applied Sciences, 2021 On-device artificial intelligence has attracted attention globally, and attempts to combine the internet of things and TinyML (machine learning) applications are increasing.
Jisu Kwon, Daejin Park
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