Publication Details
Hardware-Aware Evolutionary Approaches to Deep Neural Networks
Mrázek Vojtěch, Ing., Ph.D. (DCSY)
Piňos Michal, Ing. (DCSY)
deep neural network, evolutionary algorithm, hardware accelerator, inference,
image classification
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.
@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"
}