OTA-TinyML: Over the Air Deployment of TinyML Models and Execution on IoT Devices
IEEE Internet Computing, 2022 This article presents a novel over-the-air (OTA) technique to remotely deploy tiny ML models over Internet of Things (IoT) devices and perform tasks, such as machine learning (ML) model updates, firmware reflashing, reconfiguration, or repurposing. We discuss relevant challenges for OTA ML deployment over IoT both at the scientific and engineering ...
Bharath Sudharsan +2 more
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Hardware/Software Co-Design for TinyML Voice-Recognition Application on Resource Frugal Edge Devices
Applied Sciences (Switzerland), 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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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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TinyReptile: TinyML with Federated Meta-Learning
2023 International Joint Conference on Neural Networks (IJCNN), 2023 Tiny machine learning (TinyML) is a rapidly growing field aiming to democratize machine learning (ML) for resource-constrained microcontrollers (MCUs). Given the pervasiveness of these tiny devices, it is inherent to ask whether TinyML applications can ...
Anicic, Darko +2 more
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A Review on the emerging technology of TinyML
ACM Computing SurveysTiny Machine Learning (TinyML) is an emerging technology proposed by the scientific community for developing autonomous and secure devices that can gather, process, and provide results without transferring data to external entities. The technology aims to democratize AI by making it available to more sectors and contribute to the digital revolution of ...
Vasileios Tsoukas +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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Enhanced Diabetes Detection and Blood Glucose Prediction Using TinyML-Integrated E-Nose and Breath Analysis: A Novel Approach Combining Synthetic and Real-World Data [PDF]
BioengineeringDiabetes mellitus, a chronic condition affecting millions worldwide, necessitates continuous monitoring of blood glucose level (BGL). The increasing prevalence of diabetes has driven the development of non-invasive methods, such as electronic noses (e ...
Alberto Gudiño-Ochoa +6 more
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Efficient human activity recognition on edge devices using DeepConv LSTM architectures [PDF]
Scientific ReportsDriven by the rapid development of the Internet of Things (IoT), deploying deep learning models on resource-constrained hardware has become an increasingly critical challenge, which has propelled the emergence of TinyML as a viable solution.
Haotian Zhou +4 more
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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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TinyML-Based Lightweight AI Healthcare Mobile Chatbot Deployment [PDF]
Journal of Multidisciplinary HealthcareAnita Christaline Johnvictor,1 M Poonkodi,1 N Prem Sankar,1 Thinesh VS2 1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India; 2Arista Networks Pvt Ltd, Bangalore, IndiaCorrespondence: Anita Christaline ...
Johnvictor AC +3 more
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