Results 11 to 20 of about 374 (124)
TinyML Empowered Transfer Learning on the Edge
IEEE Open Journal of the Communications SocietyTiny machine learning (TinyML) is a promising approach to enable intelligent applications relying on Human Activity Recognition (HAR) on resource-limited and low-power Internet of Things (IoT) edge devices.
Ali M. Hayajneh +3 more
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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 the Internet of Things (IoT) and edge computing.
Harsha Yelchuri, Rashmi R
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Depth Pruning with Auxiliary Networks for Tinyml
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2022 Pruning is a neural network optimization technique that sacrifices accuracy in exchange for lower computational requirements. Pruning has been useful when working with extremely constrained environments in tinyML. Unfortunately, special hardware requirements and limited study on its effectiveness on already compact models prevent its wider adoption ...
Josen Daniel De Leon, Rowel Atienza
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TinyOL: TinyML with Online-Learning on Microcontrollers [PDF]
2021 International Joint Conference on Neural Networks (IJCNN), 2021 Tiny machine learning (TinyML) is a fast-growing research area committed to democratizing deep learning for all-pervasive microcontrollers (MCUs). Challenged by the constraints on power, memory, and computation, TinyML has achieved significant advancement in the last few years.
Haoyu Ren +2 more
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CoRR, 2021 TinyML is a fast-growing multidisciplinary field at the intersection of machine learning, hardware, and software, that focuses on enabling deep learning algorithms on embedded (microcontroller powered) devices operating at extremely low power range (mW range and below).
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2025 IEEE 49th Annual Computers, Software, and Applications Conference (COMPSAC)We train and deploy a quantized 1D convolutional neural network model to conduct speech recognition on a highly resource-constrained IoT edge device. This can be useful in various Internet of Things (IoT) applications, such as smart homes and ambient assisted living for the elderly and people with disabilities, just to name a few examples.
Andrew Barovic, Armin Moin
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IEEE AccessTiny Machine Learning (TinyML) has emerged as a transformative paradigm enabling machine learning inference directly on ultra-low-power microcontrollers and edge devices.
Chaymae Yahyati +6 more
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AI &Innovation, EarlyView.ABSTRACT The rapid evolution of the Internet of Things (IoT) has significantly advanced the field of electrocardiogram (ECG) monitoring, enabling real‐time, remote, and patient‐centric cardiac care. This paper presents a comprehensive survey of AI assisted IoT‐based ECG monitoring systems, focusing on the integration of emerging technologies such as ...
Amrita Choudhury +2 more
wiley +1 more source
A TinyML Approach to Real-Time Snoring Detection in Resource-Constrained Wearables Devices
Engineering ProceedingsThis study proposes a health monitoring system for snoring detection utilizing Tiny Machine Learning (TinyML) models, specifically designed for resource-constrained wearable Internet of Things (IoT) devices.
Timothy Malche +2 more
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Advanced Intelligent Systems, 2023 Two significant issues in biosensors that can't be solved by conventional analytical methods are selectivity among likely biological interfering molecules and background noise in clinical samples.
José Ilton de Oliveira Filho +4 more
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