Evolutionary design of complex approximate combinational circuits
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Vasicek:2016:GPEM,
-
author = "Zdenek Vasicek and Lukas Sekanina",
-
title = "Evolutionary design of complex approximate
combinational circuits",
-
journal = "Genetic Programming and Evolvable Machines",
-
year = "2016",
-
volume = "17",
-
number = "2",
-
pages = "169--192",
-
month = jun,
-
keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, approximate circuit, Binary
decision diagram, Fitness function",
-
ISSN = "1389-2576",
-
DOI = "doi:10.1007/s10710-015-9257-1",
-
size = "24 pages",
-
abstract = "Functional approximation is one of the methods
allowing designers to approximate circuits at the level
of logic behaviour. By introducing a suitable
functional approximation, power consumption, area or
delay of a circuit can be reduced if some errors are
acceptable in a particular application. As the error
quantification is usually based on an arithmetic error
metric in existing approximation methods, these methods
are primarily suitable for the approximation of
arithmetic and signal processing circuits. This paper
deals with the approximation of general logic (such as
pattern matching circuits and complex encoders) in
which no additional information is usually available to
establish a suitable error metric and hence the error
of approximation is expressed in terms of Hamming
distance between the output values produced by a
candidate approximate circuit and the accurate circuit.
We propose a circuit approximation method based on
Cartesian genetic programming in which gate-level
circuits are internally represented using directed
acyclic graphs. In order to eliminate the well-known
scalability problems of evolutionary circuit design,
the error of approximation is determined by binary
decision diagrams. The method is analysed in terms of
computational time and quality of approximation. It is
able to deliver detailed Pareto fronts showing various
compromises between the area, delay and error. Results
are presented for 16 circuits (with 27-50 inputs) that
are too complex to be approximated by means of existing
evolutionary circuit design methods.",
- }
Genetic Programming entries for
Zdenek Vasicek
Lukas Sekanina
Citations