Search Results for author: Smail Niar

Found 13 papers, 4 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

FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning

1 code implementation12 Nov 2023 Sofiane Bouaziz, Hadjer Benmeziane, Youcef Imine, Leila Hamdad, Smail Niar, Hamza Ouarnoughi

In fall detection using the MobiAct dataset, FLASH-RL outperforms FedAVG by up to 2. 82% in model's performance and reduces latency by up to 34. 75%.

Federated Learning reinforcement-learning

Grassroots Operator Search for Model Edge Adaptation

no code implementations20 Sep 2023 Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smail Niar

The mathematical instructions are then used as the basis for searching and selecting efficient replacement operators that maintain the accuracy of the original model while reducing computational complexity.

Hardware Aware Neural Architecture Search Neural Architecture Search

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

AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing

1 code implementation17 May 2023 Hadjer Benmeziane, Corey Lammie, Irem Boybat, Malte Rasch, Manuel Le Gallo, Hsinyu Tsai, Ramachandran Muralidhar, Smail Niar, Ouarnoughi Hamza, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui

Digital processors based on typical von Neumann architectures are not conducive to edge AI given the large amounts of required data movement in and out of memory.

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

A Comprehensive Survey on Hardware-Aware Neural Architecture Search

no code implementations22 Jan 2021 Hadjer Benmeziane, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smail Niar, Martin Wistuba, Naigang Wang

Arguably their most significant impact has been in image classification and object detection tasks where the state of the art results have been obtained.

Hardware Aware Neural Architecture Search Image Classification +4

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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