Evolving component library for approximate high level synthesis

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

@InProceedings{Vaverka:2016:SSCI,
  author =       "F. Vaverka and R. Hrbacek and L. Sekanina",
  booktitle =    "2016 IEEE Symposium Series on Computational
                 Intelligence (SSCI)",
  title =        "Evolving component library for approximate high level
                 synthesis",
  year =         "2016",
  abstract =     "An approximate computing approach has recently been
                 introduced for high level circuit synthesis (HLS) in
                 order to make good use of approximate circuits at
                 system and block level. It is assumed in HLS algorithms
                 that a component library containing various
                 implementations of elementary circuit components is
                 available. An open problem is how to construct such a
                 component library in the context of approximate
                 computing, where the component's error is a new design
                 variable and hence many compromise implementations
                 exist for a given component. In this paper, we first
                 introduce a multi-objective Cartesian genetic
                 programming method to create a comprehensive component
                 library containing hundreds of Pareto optimal
                 implementations of approximate 8-bit adders and
                 multipliers, where the error, area and delay are
                 simultaneously optimised. Another multi-objective
                 evolutionary algorithm is employed to solve the so
                 called binding problem of HLS, in which suitable
                 approximate components are assigned to nodes of the
                 data flow graph describing a complex digital circuit.
                 Two approaches are then proposed and compared in order
                 to reduce the size of the library of approximate
                 components. It is shown that a random sub-sampling of
                 the component library provides satisfactory results in
                 the context of our study. The proposed methods are
                 evaluated using two benchmark circuits - the reduce
                 (sum) and DCT circuits.",
  keywords =     "genetic algorithms, genetic programming, cartesian
                 genetic programming",
  DOI =          "doi:10.1109/SSCI.2016.7850168",
  month =        dec,
  notes =        "Also known as \cite{7850168}",
}

Genetic Programming entries for F Vaverka Radek Hrbacek Lukas Sekanina

Citations