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Detail publikačního výsledku
SEKANINA, L.; MRÁZEK, V.; PIŇOS, M.
Originální název
Hardware-Aware Evolutionary Approaches to Deep Neural Networks
Anglický název
Druh
Kapitola, resp. kapitoly v odborné knize
Originální abstrakt
This chapter gives an overview of evolutionary algorithm (EA) based methods applied to the design of efficient implementations of deep neural networks (DNN). We introduce various acceleration hardware platforms for DNNs developed especially for energy-efficient computing in edge devices. In addition to evolutionary optimization of their particular components or settings, we will describe neural architecture search (NAS) methods adopted to directly design highly optimized DNN architectures for a given hardware platform. Techniques that co-optimize hardware platforms and neural network architecture to maximize the accuracy-energy trade-offs will be emphasized. Case studies will primarily be devoted to NAS for image classification. Finally, the open challenges of this popular research area will be discussed.
Anglický abstrakt
Klíčová slova
deep neural network, evolutionary algorithm, hardware accelerator, inference, image classification
Klíčová slova v angličtině
Autoři
Rok RIV
2024
Vydáno
01.11.2023
Nakladatel
Springer Nature Singapore
Místo
Singapore
ISBN
978-981-9938-13-1
Kniha
Handbook of Evolutionary Machine Learning
Edice
Genetic and Evolutionary Computation
Strany od
367
Strany do
396
Strany počet
30
URL
https://link.springer.com/chapter/10.1007/978-981-99-3814-8_12
BibTex
@inbook{BUT185298, author="Lukáš {Sekanina} and Vojtěch {Mrázek} and Michal {Piňos}", title="Hardware-Aware Evolutionary Approaches to Deep Neural Networks", booktitle="Handbook of Evolutionary Machine Learning", year="2023", publisher="Springer Nature Singapore", address="Singapore", series="Genetic and Evolutionary Computation", pages="367--396", doi="10.1007/978-981-99-3814-8\{_}12", isbn="978-981-9938-13-1", url="https://link.springer.com/chapter/10.1007/978-981-99-3814-8_12" }
Dokumenty
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