Evolutionary design of complex approximate combinational circuits

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  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