Auswahl der wissenschaftlichen Literatur zum Thema „HW-Aware NAS“
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Zeitschriftenartikel zum Thema "HW-Aware NAS"
Benmeziane, Hadjer, Hamza Ouarnoughi, Kaoutar El Maghraoui und Smail Niar. „Multi-Objective Hardware-Aware Neural Architecture Search with Pareto Rank-Preserving Surrogate Models“. ACM Transactions on Architecture and Code Optimization, 11.01.2023. http://dx.doi.org/10.1145/3579853.
Der volle Inhalt der QuelleChitty-Venkata, Krishna Teja, und Arun K. Somani. „Neural Architecture Search Survey: A Hardware Perspective“. ACM Computing Surveys, 13.04.2022. http://dx.doi.org/10.1145/3524500.
Der volle Inhalt der QuelleDissertationen zum Thema "HW-Aware NAS"
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.
Der volle Inhalt der QuelleNeural Networks (NN) have become a leading force in today's digital landscape. Inspired by the human brain, their intricate design allows them to recognize patterns, make informed decisions, and even predict forthcoming scenarios with impressive accuracy. NN are widely deployed in Internet of Things (IoT) systems, further elevating interconnected devices' capabilities by empowering them to learn and auto-adapt in real-time contexts. However, the proliferation of data produced by IoT sensors makes it difficult to send them to a centralized cloud for processing. This is where the allure of edge computing becomes captivating. Processing data closer to where it originates -at the edge- reduces latency, makes real-time decisions with less effort, and efficiently manages network congestion.Integrating NN on edge devices for IoT systems enables more efficient and responsive solutions, ushering in a new age of self-sustaining Edge AI. However, Deploying NN on resource-constrained edge devices presents a myriad of challenges: (i) The inherent complexity of neural network architectures, which requires significant computational and memory capabilities. (ii) The limited power budget of IoT devices makes the NN inference prone to rapid energy depletion, drastically reducing system utility. (iii) The hurdle of ensuring harmony between NN and HW designs as they evolve at different rates. (iv) The lack of adaptability to the dynamic runtime environment and the intricacies of input data.Addressing these challenges, this thesis aims to establish innovative methods that extend conventional NN design frameworks, notably Neural Architecture Search (NAS). By integrating HW and runtime contextual features, our methods aspire to enhance NN performances while abstracting the need for the human-in-loop}. Firstly, we incorporate HW properties into the NAS by tailoring the design of NN to clock frequency variations (DVFS) to minimize energy footprint. Secondly, we leverage dynamicity within NN from a design perspective, culminating in a comprehensive Hardware-aware Dynamic NAS with DVFS features. Thirdly, we explore the potential of Graph Neural Networks (GNN) at the edge by developing a novel HW-aware NAS with distributed computing features on heterogeneous MPSoC. Fourthly, we address the SW/HW co-optimization on heterogeneous MPSoCs by proposing an innovative scheduling strategy that leverages NN adaptability and parallelism across computing units. Fifthly, we explore the prospect of ML4ML -- Machine Learning for Machine Learning by introducing techniques to estimate NN performances on edge devices using neural architectural features and ML-based predictors. Finally, we develop an end-to-end self-adaptive evolutionary HW-aware NAS framework that progressively learns the importance of NN parameters to guide the search process toward Pareto optimality effectively.Our methods can contribute to elaborating an end-to-end design framework for neural networks on edge hardware devices. They enable leveraging multiple optimization opportunities at both the software and hardware levels, thus improving the performance and efficiency of Edge AI
Buchteile zum Thema "HW-Aware NAS"
Benmeziane, Hadjer, Kaoutar El Maghraoui, Hamza Ouarnoughi und 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "HW-Aware NAS"
Benmeziane, Hadjer, Kaoutar El Maghraoui, Hamza Ouarnoughi, Smail Niar, Martin Wistuba und Naigang Wang. „Hardware-Aware Neural Architecture Search: Survey and Taxonomy“. In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/592.
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