Publication Details
Evolutionary Algorithms in Approximate Computing: A Survey
evolutionary algorithm, approximate computing, digital circuit, neural network,
optimization
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.
@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"
}