| Titre : |
Deep neural networks optimization for embedded platforms |
| Type de document : |
document électronique |
| Auteurs : |
Anouar Laouichi, Auteur ; Abderrahim Benaouda, Auteur ; Sid-Ahmed Berrani, Directeur de thèse ; Hamza Yous, Directeur de thèse |
| Editeur : |
[S.l.] : [s.n.] |
| Année de publication : |
2020 |
| Importance : |
1 fichier PDF (6.1 M) |
| Présentation : |
ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2020
Bibliogr. f. 85 - 92 |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Artificial intelligence
Deep Neural
Embedded Systems
Inference
Networks
Pruning
Quantization
Object detection
Pytorch |
| Index. décimale : |
PN00820 |
| Résumé : |
This project deals with the optimization of Deep Neural Networks for efficientembedded inference. Network Pruning and Quantization techniques are implemented underthe PyTorch environment and benchmarked on ResNet50. The obtained results, consisting ofcompression and speed-up rates, successfully validate the feasibility and the effectiveness of theconcept. To show their practical potential, the two schemes have been applied on RetinaNetobject detector. Additionally, this work demonstrates that inference can be performed at theedge by reducing the model’s memory footprint and the processing time, resulting in reducedlatency and energy consumption as well as improved data security. Hence, new horizons ofapplications in embedded systems are opened up |
Deep neural networks optimization for embedded platforms [document électronique] / Anouar Laouichi, Auteur ; Abderrahim Benaouda, Auteur ; Sid-Ahmed Berrani, Directeur de thèse ; Hamza Yous, Directeur de thèse . - [S.l.] : [s.n.], 2020 . - 1 fichier PDF (6.1 M) : ill. Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Électronique : Alger, École Nationale Polytechnique : 2020
Bibliogr. f. 85 - 92 Langues : Anglais ( eng)
| Mots-clés : |
Artificial intelligence
Deep Neural
Embedded Systems
Inference
Networks
Pruning
Quantization
Object detection
Pytorch |
| Index. décimale : |
PN00820 |
| Résumé : |
This project deals with the optimization of Deep Neural Networks for efficientembedded inference. Network Pruning and Quantization techniques are implemented underthe PyTorch environment and benchmarked on ResNet50. The obtained results, consisting ofcompression and speed-up rates, successfully validate the feasibility and the effectiveness of theconcept. To show their practical potential, the two schemes have been applied on RetinaNetobject detector. Additionally, this work demonstrates that inference can be performed at theedge by reducing the model’s memory footprint and the processing time, resulting in reducedlatency and energy consumption as well as improved data security. Hence, new horizons ofapplications in embedded systems are opened up |
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