Evolutionary functional approximation of circuits implemented into FPGAs

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@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