Soumissions récentes

• Architecture to Distribute Deep Learning Models on Containers and Virtual Machines for Industry 4.0 

  30 décembre 2023, LERAT, Jean-Sébastien; Mahmoudi, Sidi Ahmed, HE en Hainaut

  Acte de conférence ou de colloque

  

  eep learning (DL) is increasingly used in industry, especially in industry 4.0. Thanks to DL, it possible to better prevent breakdowns and manufacturing defects. DL models are becoming more and more complex and efficient, requiring significant compute resources and compute time. The use of Graphic Processing Units (GPUs) makes it possible to speed up processing but at a higher cost. An alternative ...

• KIT' À TOUT PLANTER - Le kit potager à destination des citadins 

  2020, URBAIN, Eléa, HE en Hainaut

  TFE

  

  TFE Techniques Graphiques

• RÉALISATION D'UN JEU DE SOCIÉTÉ COOPÉRATIF AVEC TABLETOP SIMULATOR 

  2020, Meurisse, Clément, HE en Hainaut

  TFE

  

  TFE Techniques Graphiques

• Création d’un site web et conception d’armes pour l’ASBL 7RAPL 

  2021, Swertvaegher, Axelle, HE en Hainaut

  TFE

  

  TFE Techniques Graphiques

• Conception de l'identité graphique et de la signalétique du nouveau MICE TournaiXpo 

  2021, Grimmonpré, Thibaut, HE en Hainaut

  TFE

  

  TFE Techniques Graphiques

• LES USAGES DU BRUIT EN INFOGRAPHIE 

  2021, Dejonckheere, Ludwig, HE en Hainaut

  TFE

  

  TFE Techniques Graphiques

• Distributed Deep Learning: From Single-Node to Multi-Node Architecture 

  2022, LERAT, Jean-Sébastien; Mahmoudi, Sidi Ahmed; Mahmoudi, Saïd, HE en Hainaut

  Article scientifique

  

  During the last years, deep learning (DL) models have been used in several applications with large datasets and complex models. These applications require methods to train models faster, such as distributed deep learning (DDL). This paper proposes an empirical approach aiming to measure the speedup of DDL achieved by using different parallelism strategies on the nodes. Local parallelism is considered ...