Bibliografías temáticas / HW-Aware NAS

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Literatura académica sobre el tema "HW-Aware NAS"

Autor: Grafiati
Publicado: 7 de julio de 2024
Última modificación: 31 de julio de 2025

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Artículos de revistas sobre el tema "HW-Aware NAS"

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Benmeziane, Hadjer, Hamza Ouarnoughi, Kaoutar El Maghraoui, and Smail Niar. "Multi-Objective Hardware-Aware Neural Architecture Search with Pareto Rank-Preserving Surrogate Models." ACM Transactions on Architecture and Code Optimization, January 11, 2023. http://dx.doi.org/10.1145/3579853.

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Deep learning (DL) models such as convolutional neural networks (ConvNets) are being deployed to solve various computer vision and natural language processing tasks at the edge. It is a challenge to find the right DL architecture that simultaneously meets the accuracy, power and performance budgets of such resource-constrained devices. Hardware-aware Neural Architecture Search (HW-NAS) has recently gained steam by automating the design of efficient DL models for a variety of target hardware platform. However, such algorithms require excessive computational resources. Thousands of GPU days are
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2

Chitty-Venkata, Krishna Teja, and Arun K. Somani. "Neural Architecture Search Survey: A Hardware Perspective." ACM Computing Surveys, April 13, 2022. http://dx.doi.org/10.1145/3524500.

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We review the problem of automating hardware-aware architectural design process of Deep Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design has led to advancements in many fields such as computer vision, virtual reality, and autonomous driving. The end-to-end design process of a CNN is a challenging and time-consuming task as it requires expertise in multiple areas such as signal and image processing, neural networks, and optimization. At the same time, several hardware platforms, general- and special-purpose, have equally contributed to the training and de
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Tesis sobre el tema "HW-Aware NAS"

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Bouzidi, Halima. "Efficient Deployment of Deep Neural Networks on Hardware Devices for Edge AI." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2024. http://www.theses.fr/2024UPHF0006.

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Les réseaux de neurones (RN) sont devenus une force dominante dans le monde de la technologie. Inspirés par le cerveau humain, leur conception complexe leur permet d’apprendre des motifs, de prendre des décisions et même de prévoir des scénarios futurs avec une précision impressionnante. Les RN sont largement déployés dans les systèmes de l'Internet des Objets (IoT pour Internet of Things), renforçant davantage les capacités des dispositifs interconnectés en leur donnant la capacité d'apprendre et de s'auto-adapter dans un contexte temps réel. Cependant, la prolifération des données produites
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Capítulos de libros sobre el tema "HW-Aware NAS"

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Archet, Agathe, Nicolas Ventroux, Nicolas Gac, and François Orieux. "A Practical HW-Aware NAS Flow for AI Vision Applications on Embedded Heterogeneous SoCs." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-87897-8_4.

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2

Benmeziane, Hadjer, Kaoutar El Maghraoui, Hamza Ouarnoughi, and Smail Niar. "Pareto Rank-Preserving Supernetwork for Hardware-Aware Neural Architecture Search." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230276.

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In neural architecture search (NAS), training every sampled architecture is very time-consuming and should be avoided. Weight-sharing is a promising solution to speed up the evaluation process. However, training the supernetwork incurs many discrepancies between the actual ranking and the predicted one. Additionally, efficient deep-learning engineering processes require incorporating realistic hardware-performance metrics into the NAS evaluation process, also known as hardware-aware NAS (HW-NAS). In HW-NAS, estimating task-specific performance and hardware efficiency are both required. This pa
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Actas de conferencias sobre el tema "HW-Aware NAS"

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Benmeziane, Hadjer, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smail Niar, Martin Wistuba, and Naigang Wang. "Hardware-Aware Neural Architecture Search: Survey and Taxonomy." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/592.

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There is no doubt that making AI mainstream by bringing powerful, yet power hungry deep neural networks (DNNs) to resource-constrained devices would required an efficient co-design of algorithms, hardware and software. The increased popularity of DNN applications deployed on a wide variety of platforms, from tiny microcontrollers to data-centers, have resulted in multiple questions and challenges related to constraints introduced by the hardware. In this survey on hardware-aware neural architecture search (HW-NAS), we present some of the existing answers proposed in the literature for the foll
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