7 citations · 7 across the 4 of their papers we have counts for
5 papers
Balancing Stability and Plasticity in Sequentially Trained Early-Exiting Neural Networks
Alaa Zniber, Ouassim Karrakchou, Mounir Ghogho
Early-exiting neural networks enable adaptive inference by allowing inputs to exit at intermediate classifiers, reducing computation for easy samples while maintaining high accurac…
Hardware-Algorithm Co-Optimization of Early-Exit Neural Networks for Multi-Core Edge Accelerators
Alaa Zniber, Arne Symons, Ouassim Karrakchou +2
The deployment of Early-Exiting Neural Networks (EENNs) on edge accelerators requires optimizing not only the network architecture but also its hardware deployment. Exit configurat…
Collaborative P4-SDN DDoS Detection and Mitigation with Early-Exit Neural Networks
Ouassim Karrakchou, Alaa Zniber, Anass Sebbar +1
Distributed Denial of Service (DDoS) attacks pose a persistent threat to network security, requiring timely and scalable mitigation strategies. In this paper, we propose a novel co…
Early Exiting Predictive Coding Neural Networks for Edge AI
Alaa Zniber, Mounir Ghogho, Ouassim Karrakchou +1
The Internet of Things is transforming various fields, with sensors increasingly embedded in wearables, smart buildings, and connected equipment. While deep learning enables valuab…
Dynamic nsNet2: Efficient Deep Noise Suppression with Early Exiting
Riccardo Miccini, Alaa Zniber, Clément Laroche +6
Although deep learning has made strides in the field of deep noise suppression, leveraging deep architectures on resource-constrained devices still proved challenging. Therefore, w…