Combinative model compression approach for enhancing 1D CNN efficiency for EIT-based Hand Gesture Recognition on IoT edge devices

Tiny Machine Learning is rapidly evolving in edge computing and intelligent Internet of Things (IoT) devices. This paper investigates model compression techniques with the aim of determining their compatibility when combined, and identifying an effective approach to improve inference speed and energ...

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Bibliographic Details
Published in:	Internet of things (Amsterdam. Online) Vol. 28; p. 101403
Main Authors:	Mnif, Mahdi, Sahnoun, Salwa, Ben Saad, Yasmine, Fakhfakh, Ahmed, Kanoun, Olfa
Format:	Journal Article
Language:	English
Published:	Elsevier B.V 01.12.2024
Subjects:	1D CNN Edge computing EIT Energy efficiency Hand Gesture Recognition Inference time IoT devices Lightweight models Model compression techniques Tiny ML EIT Tiny ML Hand Gesture Recognition Lightweight models IoT devices Energy efficiency Inference time 1D CNN Model compression techniques Edge computing
ISSN:	2542-6605, 2542-6605
Online Access:	Get full text
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