Evolutionary functional approximation of circuits implemented into FPGAs
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @InProceedings{Vasicek:2016:SSCI,
-
author = "Z. Vasicek and V. Mrazek and L. Sekanina",
-
booktitle = "2016 IEEE Symposium Series on Computational
Intelligence (SSCI)",
-
title = "Evolutionary functional approximation of circuits
implemented into FPGAs",
-
year = "2016",
-
abstract = "In many applications it is acceptable to allow a small
error in the result if significant improvements are
obtained in terms of performance, area or energy
efficiency. Exploiting this principle is particularly
important for FPGA-based solutions that are inherently
subject to many resources-oriented constraints. This
paper devises an automated method that enables to
approximate circuit components which are often
implemented in multiple instances in FPGA-based
accelerators. The approximation process starts with a
fully functional gate-level circuit, which is
approximated by means of Cartesian Genetic Programming
reflecting the error metric and constraints formulated
by the user. The evolved circuits are then implemented
for a particular FPGA by common FPGA synthesis and
optimisation tools. It is shown using five different
FPGA tools, that the approximations obtained by CGP
working at the gate level are preserved at the level
look-up tables of FPGAs. The proposed method is
evaluated in the task of 8-bit adder, 8-bit multiplier,
9-input median and 25-input median approximation.",
-
keywords = "genetic algorithms, genetic programming, Cartesian
genetic programming, EHW",
-
DOI = "doi:10.1109/SSCI.2016.7850173",
-
month = dec,
-
notes = "Also known as \cite{7850173}",
- }
Genetic Programming entries for
Zdenek Vasicek
Vojtech Mrazek
Lukas Sekanina
Citations