Search Results for author: Halima Bouzidi

Found 7 papers, 2 papers with code

SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search

no code implementations20 Feb 2024 Halima Bouzidi, Smail Niar, Hamza Ouarnoughi, El-Ghazali Talbi

Our SONATA has seen up to sim$93. 6% Pareto dominance over the native NSGA-II, further stipulating the importance of self-adaptive evolution operators in HW-aware NAS.

Evolutionary Algorithms Hardware Aware Neural Architecture Search +1

Harmonic-NAS: Hardware-Aware Multimodal Neural Architecture Search on Resource-constrained Devices

1 code implementation12 Sep 2023 Mohamed Imed Eddine Ghebriout, Halima Bouzidi, Smail Niar, Hamza Ouarnoughi

In this paper, we propose Harmonic-NAS, a framework for the joint optimization of unimodal backbones and multimodal fusion networks with hardware awareness on resource-constrained devices.

General Classification Multimodal Text and Image Classification +1

MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for Heterogeneous MPSoC Deployment

no code implementations16 Jul 2023 Mohanad Odema, Halima Bouzidi, Hamza Ouarnoughi, Smail Niar, Mohammad Abdullah Al Faruque

To achieve this, MaGNAS employs a two-tier evolutionary search to identify optimal GNNs and mapping pairings that yield the best performance trade-offs.

Graph Learning Neural Architecture Search

HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling

1 code implementation6 Dec 2022 Halima Bouzidi, Mohanad Odema, Hamza Ouarnoughi, Mohammad Abdullah Al Faruque, Smail Niar

Dynamic neural networks (DyNNs) have become viable techniques to enable intelligence on resource-constrained edge devices while maintaining computational efficiency.

Computational Efficiency Edge-computing +1

Performance Prediction for Convolutional Neural Networks in Edge Devices

no code implementations21 Oct 2020 Halima Bouzidi, Hamza Ouarnoughi, Smail Niar, Abdessamad Ait El Cadi

In this paper, we present and compare five (5) of the widely used Machine Learning based methods for execution time prediction of CNNs on two (2) edge GPU platforms.

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