Assessing the Efficacy of TinyML Implementations on STM32 Microcontrollers: A Performance Evaluation Study

In recent years, there has been a growing need for efficient deployment of deep learning models on resource-limited edge devices. This trend has prompted the development of NVIDIA's TAO Toolkit and TensorFlow Lite Micro framework as promising solutions for running deep-learning models on STM32...

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Veröffentlicht in:	International Conference on Advanced Technologies for Signal and Image Processing (Online) Jg. 1; S. 267 - 271
Hauptverfasser:	Ali, Diouani, Ridha, El Hamdi, Mohamed, Njah
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 11.07.2024
Schlagworte:	Accuracy Analytical models Computational modeling Deep learning Deep Learning Models Microcontrollers Model Optimization NVIDIA's TAO Toolkit Performance evaluation Resource-limited Edge Devices STM32 Microcontrollers TensorFlow Lite Micro Tiny machine learning
ISSN:	2687-878X
Online-Zugang:	Volltext
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