57 citations · 64 across the 6 of their papers we have counts for
9 papers
Deep Learning for Anomaly Detection in Railway Systems: A Structured Survey
Ammar Bouketta, Smail Niar, Hamza Ouarnoughi
Ensuring safe and reliable operation of modern railway systems increasingly relies on data-driven monitoring and intelligent fault detection. Deep learning has emerged as an effect…
Cycle-Aware Autoencoder with Cross-SignalConsistency for Railway Door Anomaly Detection
Ammar Bouketta, Smail Niar, Hamza Ouarnoughi +1
Passenger access doors are safety-critical subsystems in railway vehicles, yet detecting abnormal door behavior in real operation is challenging because faults are rare, diverse, a…
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search
Halima Bouzidi, Smail Niar, Hamza Ouarnoughi +1
Recent advancements in Artificial Intelligence (AI), driven by Neural Networks (NN), demand innovative neural architecture designs, particularly within the constrained environments…
MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment
Mohanad Odema, Halima Bouzidi, Hamza Ouarnoughi +2
Graph Neural Networks (GNNs) are becoming increasingly popular for vision-based applications due to their intrinsic capacity in modeling structural and contextual relations between…
HyT-NAS: Hybrid Transformers Neural Architecture Search for Edge Devices
Lotfi Abdelkrim Mecharbat, Hadjer Benmeziane, Hamza Ouarnoughi +1
Vision Transformers have enabled recent attention-based Deep Learning (DL) architectures to achieve remarkable results in Computer Vision (CV) tasks. However, due to the extensive…
A Comprehensive Survey on Hardware-Aware Neural Architecture Search
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi +3
Neural Architecture Search (NAS) methods have been growing in popularity. These techniques have been fundamental to automate and speed up the time consuming and error-prone process…
Performance Prediction for Convolutional Neural Networks in Edge Devices
Halima Bouzidi, Hamza Ouarnoughi, Smail Niar +1
Running Convolutional Neural Network (CNN) based applications on edge devices near the source of data can meet the latency and privacy challenges. However due to their reduced comp…
A WCET-aware cache coloring technique for reducing interference in real-time systems
Fabien Bouquillon, Clément Ballabriga, Giuseppe Lipari +1
The predictability of a system is the condition to give saferbound on worst case execution timeof real-time tasks which are running on it. Commercial off-the-shelf(COTS) processors…
Proceedings of the Workshop on High Performance Energy Efficient Embedded Systems (HIP3ES) 2015
Francisco Corbera, Andrés Rodríguez, Rafael Asenjo +19
Proceedings of the Workshop on High Performance Energy Efficient Embedded Systems (HIP3ES) 2015. Amsterdam, January 21st. Collocated with HIPEAC 2015 Conference.