57 citations · 65 across the 12 of their papers we have counts for
14 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…
Combining Neural Architecture Search and Automatic Code Optimization: A Survey
Inas Bachiri, Hadjer Benmeziane, Smail Niar +3
Deep Learning models have experienced exponential growth in complexity and resource demands in recent years. Accelerating these models for efficient execution on resource-constrain…
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…
FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning
Sofiane Bouaziz, Hadjer Benmeziane, Youcef Imine +3
Federated Learning (FL) has emerged as a promising Machine Learning paradigm, enabling multiple users to collaboratively train a shared model while preserving their local data. To…
Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices
Mohamed Imed Eddine Ghebriout, Halima Bouzidi, Smail Niar +1
The recent surge of interest surrounding Multimodal Neural Networks (MM-NN) is attributed to their ability to effectively process and integrate multiscale information from diverse…
Grassroots Operator Search for Model Edge Adaptation
Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi +1
Hardware-aware Neural Architecture Search (HW-NAS) is increasingly being used to design efficient deep learning architectures. An efficient and flexible search space is crucial to…
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…
Treasure What You Have: Exploiting Similarity in Deep Neural Networks for Efficient Video Processing
Hadjer Benmeziane, Halima Bouzidi, Hamza Ouarnoughi +2
Deep learning has enabled various Internet of Things (IoT) applications. Still, designing models with high accuracy and computational efficiency remains a significant challenge, es…
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…
Map-and-Conquer: Energy-Efficient Mapping of Dynamic Neural Nets onto Heterogeneous MPSoCs
Halima Bouzidi, Mohanad Odema, Hamza Ouarnoughi +2
Heterogeneous MPSoCs comprise diverse processing units of varying compute capabilities. To date, the mapping strategies of neural networks (NNs) onto such systems are yet to exploi…
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling
Halima Bouzidi, Mohanad Odema, Hamza Ouarnoughi +2
Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency. In many cases,…
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…