Results 21 to 30 of about 2,597 (210)
TinyML: Tools, Applications, Challenges, and Future Research Directions [PDF]
, 2023 In recent years, Artificial Intelligence (AI) and Machine learning (ML) have gained significant interest from both, industry and academia. Notably, conventional ML techniques require enormous amounts of power to meet the desired accuracy, which has ...
Iyer, Sridhar +4 more
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A multi-microcontroller-based hardware for deploying Tiny machine learning model [PDF]
, 2023 The tiny machine learning (TinyML) has been considered to applied on the edge devices where the resource-constrained micro-controller units (MCUs) were used. Finding a good platform to deploy the TinyML effectively is very crucial.
Nguyen, Chi-Ngon +5 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
doaj +1 more source
Software Engineering Approaches for TinyML based IoT Embedded Vision: A Systematic Literature Review [PDF]
, 2022 Internet of Things (IoT) has catapulted human ability to control our environments through ubiquitous sensing, communication, computation, and actuation. Over the past few years, IoT has joined forces with Machine Learning (ML) to embed deep intelligence ...
Eisty, Nasir U. +1 more
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Dynamic AI-IoT:enabling updatable AI models in ultra-low-power 5G IoT devices [PDF]
, 2023 This article addresses the challenge of integrating dynamic AI capabilities into ultralow-power (ULP) IoT devices, a critical necessity in the rapidly evolving landscape of 5G and potential 6G technologies. We introduce the Dynamic AI-IoT architecture, a
Alcaraz-Calero, Jose M. +2 more
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CODE: description language for wireless collaborating objects [PDF]
, 2005 This paper introduces CODE, a Description Language for Wireless Collaborating Objects (WCO), with the specific aim of enabling service management in smart environments.
Havinga, Paul +2 more
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TinyML: From Basic to Advanced Applications [PDF]
, 2021 TinyML tiene como objetivo la implementación de aplicaciones de aprendizaje automático en dispositivos de poco tamaño y baja potencia, como los microcontroladores. Normalmente los dispositivos periféricos necesitan estar conectados a centro de datos para
Monfort Grau, Marc
core
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
Unlocking Doors: A TinyML-based Approach for real-time Face Mask Detection in Door Lock Systems [PDF]
, 2023 In response to the rapid spread of coronaviruses, including COVID-19 and seasonal common cold viruses, this article introduces a proposed system for enhancing door lock systems using TinyML technology for real-time face mask detection.
Benaly, Mohamed +4 more
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Tiny Machine Learning Environment: Enabling Intelligence on Constrained Devices [PDF]
, 2023 Running machine learning algorithms (ML) on constrained devices at the extreme edge of the network is problematic due to the computational overhead of ML algorithms, available resources on the embedded platform, and application budget (i.e., real-time ...
SAKR, FOUAD
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