Results 11 to 20 of about 793 (174)

TinyML Empowered Transfer Learning on the Edge

IEEE Open Journal of the Communications Society
Tiny 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, Maryam Hafeez, Syed Ali Raza Zaidi, Des McLernon   +3 more
doaj   +3 more sources

A review of TinyML

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
openaire   +2 more sources

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, John G. Breslin, Mehreen Tahir, Muhammad Intizar Ali, Omer F. Rana, Schahram Dustdar, Rajiv Ranjan 0001   +6 more
openaire   +1 more source

TinyML Platforms Benchmarking

, 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.
Osman, Anas, Abid, Usman, Gemma, Luca, Perotto, Matteo, Brunelli, Davide   +4 more
openaire   +3 more sources

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
openaire   +2 more sources

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, Darko Anicic, Thomas A. Runkler   +2 more
openaire   +2 more sources

TinyML for Ubiquitous Edge AI

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).
openaire   +2 more sources

Edge AI for Climate-Aware ET0 Forecasting and Autonomous Precision Irrigation: A Hardware-Software Co-Design for Autonomous Precision Irrigation [PDF]

E3S Web of Conferences
Precision agriculture is hindered by its dependence on centralized cloud infrastructures, which prevents deployment of advanced deep learning (DL) in disconnected, resource‑constrained rural environments.
Hodouto Horatio Harley Koffivi, Cherouat Abel, Dzagli Milohum Mikesokpo, Bassir David   +3 more
doaj   +1 more source

Mapping the Convergence of Artificial Intelligence, IoT, and Embedded Systems: A Comprehensive Bibliometric Analysis (2015-2025) [PDF]

E3S Web of Conferences
Background: The rapid convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and Embedded Systems has birthed the era of "Edge Intelligence.
Nouayti Mohamed, Aynaou Anass, Nouayti Nordine, Adnani Younes   +3 more
doaj   +1 more source

IoT Makers: A Collaborative Learning Experience with TinyML

Shodh Sari
Traditional teaching methods often fail to fully engage students in the field of IoT, particularly when it comes to applying machine learning at the edge. This paper presents an innovative pedagogical approach titled “IoT Makers,” aimed at MSc Artificial
Dr. Helen K. Joy
doaj   +1 more source