Hardware-Aware Evolutionary Approaches to Deep Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149510" target="_blank" >RIV/00216305:26230/23:PU149510 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-981-99-3814-8_12" target="_blank" >https://link.springer.com/chapter/10.1007/978-981-99-3814-8_12</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-99-3814-8_12" target="_blank" >10.1007/978-981-99-3814-8_12</a>
Alternative languages
Result language
angličtina
Original language name
Hardware-Aware Evolutionary Approaches to Deep Neural Networks
Original language description
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.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA21-13001S" target="_blank" >GA21-13001S: Automated design of hardware accelerators for resource-aware machine learning</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Handbook of Evolutionary Machine Learning
ISBN
978-981-9938-13-1
Number of pages of the result
30
Pages from-to
367-396
Number of pages of the book
768
Publisher name
Springer Nature Singapore
Place of publication
Singapore
UT code for WoS chapter
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