Evolutionary Algorithms in Approximate Computing: A Survey
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F21%3APU142918" target="_blank" >RIV/00216305:26230/21:PU142918 - isvavai.cz</a>
Result on the web
<a href="https://jics.org.br/ojs/index.php/JICS/article/view/499" target="_blank" >https://jics.org.br/ojs/index.php/JICS/article/view/499</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.29292/jics.v16i2.499" target="_blank" >10.29292/jics.v16i2.499</a>
Alternative languages
Result language
angličtina
Original language name
Evolutionary Algorithms in Approximate Computing: A Survey
Original language description
In recent years, many design automation methods have been developed to routinely create approximate implementations of circuits and programs that show excellent trade-offs between the quality of output and required resources. This paper deals with evolutionary approximation as one of the popular approximation methods. The paper provides the first survey of evolutionary algorithm (EA)-based approaches applied in the context of approximate computing. The survey reveals that EAs are primarily applied as multi-objective optimizers. We propose to divide these approaches into two main classes: (i) parameter optimization in which the EA optimizes a vector of system parameters, and (ii) synthesis and optimization in which EA is responsible for determining the architecture and parameters of the resulting system. The evolutionary approximation has been applied at all levels of design abstraction and in many different applications. The neural architecture search enabling the automated hardware-aware design of approximate deep neural networks was identified as a newly emerging topic in this area.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
20206 - Computer hardware and architecture
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
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Integrated Circuits and Systems
ISSN
1872-0234
e-ISSN
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Volume of the periodical
16
Issue of the periodical within the volume
2
Country of publishing house
BR - BRAZIL
Number of pages
12
Pages from-to
1-12
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85114040847