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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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DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning
Sensors, 2023 Driver drowsiness is one of the main causes of traffic accidents today. In recent years, driver drowsiness detection has suffered from issues integrating deep learning (DL) with Internet-of-things (IoT) devices due to the limited resources of IoT devices,
Norah N Alajlan, Dina M Ibrahim
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CoRR, 2022 In this current technological world, the application of machine learning is becoming ubiquitous. Incorporating machine learning algorithms on extremely low-power and inexpensive embedded devices at the edge level is now possible due to the combination of
R, Rashmi, Yelchuri, Harsha
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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
doaj +2 more sources
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 ...
Nikolaos Schizas +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
doaj +2 more sources
Exploring opportunities in TinyML [PDF]
, 2022 Internet of Things (IoT) has acquired useful and powerful advances thanks to the Machine Learning (ML) implementations. But the implementation of Machine Learning in IoT devices with data centers has some serious problems (data privacy, network ...
Rubio Serrano, Juan Diego
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Lecture Notes in Electrical Engineering, 2022 Recent advances in state-of-the-art ultra-low power embedded devices for machine learning (ML) have permitted a new class of products whose key features enable ML capabilities on microcontrollers with less than 1 mW power consumption (TinyML). TinyML provides a unique solution by aggregating and analyzing data at the edge on low-power embedded devices.
Luca Gemma, Davide Brunelli
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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
doaj +2 more sources
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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