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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Veröffentlicht in:Internet of things (Amsterdam. Online) Jg. 28; S. 101403
Hauptverfasser: Mnif, Mahdi, Sahnoun, Salwa, Ben Saad, Yasmine, Fakhfakh, Ahmed, Kanoun, Olfa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2024
Schlagworte:
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
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Zusammenfassung: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 energy efficiency within IoT edge devices. The study is carried out on the application scenario of Hand Gesture Recognition (HGR) based on Electrical Impedance Tomography (EIT), which involves complex signal processing and needs real-time processing and energy efficiency. Therefore, a customized 1-Dimensional Convolutional Neural Network (1D CNN) HGR classification model has been designed. An approach based on strategically combining model compression techniques was then implemented resulting in a model customized for faster inference and improved energy efficiency for IoT embedded devices. The model size became compact at 10.42 kB, resulting in a substantial size reduction of 98.8%, and an inference gain of 94.73% on a personal computer with approximately 8.56% decrease in accuracy. The approach of combinative model compression techniques was applied to a wide range of edge-computing IoT devices with limited processing power, resulting in a significant improvement in model execution speed and energy efficiency for these devices. Specifically, there was an average power consumption gain of 52% for Arduino Nano BLE and 34.05% for Raspberry Pi 4. Inference time was halved for Arduino Nano BLE Sense, Nicla Sense, and Raspberry Pi 4, with a remarkable gain of 94% for ESP32.
ISSN:2542-6605
2542-6605
DOI:10.1016/j.iot.2024.101403