Development of a customized processor architecture for accelerating genetic algorithms

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

@Article{Kavvadias2007347,
  author =       "Nikolaos Kavvadias and Vasiliki Giannakopoulou and 
                 Spiridon Nikolaidis",
  title =        "Development of a customized processor architecture for
                 accelerating genetic algorithms",
  journal =      "Microprocessors and Microsystems",
  volume =       "31",
  number =       "5",
  pages =        "347--359",
  year =         "2007",
  ISSN =         "0141-9331",
  DOI =          "DOI:10.1016/j.micpro.2006.12.002",
  URL =          "http://www.sciencedirect.com/science/article/B6V0X-4MT5K1Y-1/2/c2a2d447c74f5cfcb3dec1eb0125163f",
  keywords =     "genetic algorithms, genetic programming, 89.20.Ff,
                 Embedded systems, Field-programmable gate arrays,
                 Application-specific processors, Hardware description
                 languages",
  abstract =     "In this paper, a new programmable RISC processor
                 architecture named VGP-I is proposed, aiming to the
                 acceleration of genetic algorithms in embedded systems.
                 Compared to other GA engines, the VGP-I specification
                 defines a compact instruction set supporting multiple
                 operator types, with scalable instruction encodings,
                 programmer-visible and auxiliary registers and optional
                 extensions. Apart from the programmable accelerator
                 approach, VGP-I instructions have been tightly
                 integrated to the Nios II soft-core processor as well.
                 For performance assessment, a cycle-accurate reference
                 VGP-I model has been developed while VGP-I subsets have
                 been realized on a prototype microarchitecture and as
                 Nios II custom instructions, both verified on
                 programmable logic. Performance improvements on the
                 execution of genetic operators are typically at the
                 level of two orders of magnitude with application
                 kernels written in ANSI C being accelerated by about 20
                 times due to the usage of GA instruction set
                 extensions.",
}

Genetic Programming entries for Nikolaos Kavvadias Vasiliki Giannakopoulou Spiridon Nikolaidis

Citations