| Autor: |
Icaro Camelo, Ana-Maria Cretu |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Engineering Proceedings, Vol 58, Iss 1, p 6 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2673-4591 |
| DOI: |
10.3390/ecsa-10-16202 |
| Popis: |
The goal of this paper is to evaluate various compact CNN architectures for object recognition trained on a small resource-constrained platform, the NVIDIA Jetson Xavier. Rigorous experimentation identifies the best compact CNN models that balance accuracy and speed on embedded IoT devices. The key objectives are to analyze resource usage such as CPU/GPU and RAM used to train models, the performance of the CNNs, identify trade-offs, and find optimized deep learning solutions tailored for training and real-time inference on edge devices with tight resource constraints. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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