Performance characterization on embedded systems for Edge AI person-detection models
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| Auteurs: | , |
|---|---|
| Format: | artículo original |
| Statut: | Versión publicada |
| Date de publication: | 2025 |
| Description: | This paper presents a hardware performance characterization for two Edge AI platforms: Raspberry Pi 4 and NVIDIA Jetson Nano, for the task of automatic people detection using a deep learning model. For comparison purposes, we use the MLPerf Inference Benchmark evaluation system. The characterization considers the results from an SSD-Mobilenet object-detection model using two different datasets, one with 80 different object classes and another with only people. Comparison metrics consider model accuracy, latency, queries processed per second, and samples processed per second under the evaluation of different execution scenarios. |
| Pays: | Portal de Revistas TEC |
| Institution: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Langue: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/7754 |
| Accès en ligne: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/7754 |
| Mots-clés: | EdgeIA NVIDIA Jetson Nano Raspberry Pi 4 MLPerf Inference Benchmark SSD-MobileNet Edge AI MLPerf Inference Benchmark |