Automatic design of approximate circuits by means of multi-objective evolutionary algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Radek Hrbacek and Vojtech Mrazek and Zdenek Vasicek",
  booktitle =    "2016 International Conference on Design and Technology
                 of Integrated Systems in Nanoscale Era (DTIS)",
  title =        "Automatic design of approximate circuits by means of
                 multi-objective evolutionary algorithms",
  year =         "2016",
  abstract =     "Recently, power efficiency has become the most
                 important parameter of many real circuits. At the same
                 time, a wide range of applications capable of
                 tolerating imperfections has spread out especially in
                 multimedia. Approximate computing, an emerging
                 paradigm, takes advantage of relaxed functional
                 requirements to make computer systems more efficient in
                 terms of energy consumption, speed or complexity. As a
                 result, a variety of trade-offs between error and
                 efficiency can be found. In this paper, a design method
                 based on a multi-objective evolutionary algorithm is
                 proposed. For a given circuit, the method is able to
                 produce a set of Pareto optimal solutions in terms of
                 the error, power consumption and delay. The proposed
                 design method uses Cartesian Genetic Programming for
                 the circuit representation and a modified NSGA-II
                 algorithm for design space exploration. The method is
                 used to design Pareto optimal approximate versions of
                 arithmetic circuits such as multipliers and adders.",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  DOI =          "doi:10.1109/DTIS.2016.7483885",
  month =        apr,
  notes =        "Also known as \cite{7483885}",

Genetic Programming entries for Radek Hrbacek Vojtech Mrazek Zdenek Vasicek