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Publication detail
SEKANINA, L.
Original Title
Evolutionary Algorithms in Approximate Computing: A Survey
Type
journal article in Scopus
Language
English
Original Abstract
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.
Keywords
evolutionary algorithm, approximate computing, digital circuit, neural network, optimization
Authors
Released
23. 8. 2021
ISBN
1872-0234
Periodical
Journal of Integrated Circuits and Systems
Year of study
16
Number
2
State
Federative Republic of Brazil
Pages from
1
Pages to
12
Pages count
URL
https://jics.org.br/ojs/index.php/JICS/article/view/499
BibTex
@article{BUT175795, author="Lukáš {Sekanina}", title="Evolutionary Algorithms in Approximate Computing: A Survey", journal="Journal of Integrated Circuits and Systems", year="2021", volume="16", number="2", pages="1--12", doi="10.29292/jics.v16i2.499", issn="1872-0234", url="https://jics.org.br/ojs/index.php/JICS/article/view/499" }