Optimizing L1 cache for embedded systems through grammatical evolution

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@Article{Diaz-Alvarez:2016:SC,
  author =       "Josefa {Diaz Alvarez} and J. Manuel Colmenar and 
                 Jose L. Risco-Martin and Juan Lanchares and Oscar Garnica",
  title =        "Optimizing L1 cache for embedded systems through
                 grammatical evolution",
  journal =      "Soft Computing",
  year =         "2016",
  volume =       "20",
  number =       "6",
  pages =        "2451--2465",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, EHW",
  ISSN =         "1432-7643",
  DOI =          "doi:10.1007/s00500-015-1653-1",
  size =         "15 pages",
  abstract =     "Nowadays, embedded systems are provided with cache
                 memories that are large enough to influence in both
                 performance and energy consumption as never occurred
                 before in this kind of systems. In addition, the cache
                 memory system has been identified as a component that
                 improves those metrics by adapting its configuration
                 according to the memory access patterns of the
                 applications being run. However, given that cache
                 memories have many parameters which may be set to a
                 high number of different values, designers are faced
                 with a wide and time-consuming exploration space. In
                 this paper, we propose an optimization framework based
                 on Grammatical Evolution (GE) which is able to
                 efficiently find the best cache configurations for a
                 given set of benchmark applications. This metaheuristic
                 allows an important reduction of the optimization
                 runtime obtaining good results in a low number of
                 generations. Besides, this reduction is also increased
                 due to the efficient storage of evaluated caches.
                 Moreover, we selected GE because the plasticity of the
                 grammar eases the creation of phenotypes that form the
                 call to the cache simulator required for the evaluation
                 of the different configurations. Experimental results
                 for the Mediabench suite show that our proposal is able
                 to find cache configurations that obtain an average
                 improvement of 62percent versus a real world baseline
                 configuration",
  notes =        "See \cite{Diaz-Alvarez:2016:JSS}. prior work
                 \cite{1570271} \cite{Diaz:2010:ICEC}",
}

Genetic Programming entries for Josefa Diaz Alvarez J Manuel Colmenar Jose L Risco-Martin J Lanchares Oscar Garnica

Citations