Publication Details

Hardware-Aware Evolutionary Approaches to Deep Neural Networks

SEKANINA, L.; MRÁZEK, V.; PIŇOS, M. Hardware-Aware Evolutionary Approaches to Deep Neural Networks. In Handbook of Evolutionary Machine Learning. Genetic and Evolutionary Computation. Singapore: Springer Nature Singapore, 2023. p. 367-396. ISBN: 978-981-9938-13-1.
Czech title
Evoluční přístupy k hlubokým neuronovým sítím s ohledem na hardware
Type
book chapter
Language
English
Authors
URL
Keywords

deep neural network, evolutionary algorithm, hardware accelerator, inference, image classification

Abstract

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.

Published
2023
Pages
367–396
Book
Handbook of Evolutionary Machine Learning
Series
Genetic and Evolutionary Computation
ISBN
978-981-9938-13-1
Publisher
Springer Nature Singapore
Place
Singapore
DOI
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"
}
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