Architecture performance prediction using evolutionary artificial neural networks

  • Authors:

  • P. A. Castillo;A. M. Mora;J. J. Merelo;J. L. J. Laredo;M. Moreto;F. J. Cazorla;M. Valero;S. A. McKee

  • Affiliations:

  • Architecture and Computer Technology Department, University of Granada;Architecture and Computer Technology Department, University of Granada;Architecture and Computer Technology Department, University of Granada;Architecture and Computer Technology Department, University of Granada;Computer Architecture Department, Technical University of Catalonia, HiPEAC European Network of Excellence;Barcelona Supercomputing Center;Computer Architecture Department, Technical University of Catalonia, HiPEAC European Network of Excellence and Barcelona Supercomputing Center;Cornell University

  • Venue:
  • Evo'08 Proceedings of the 2008 conference on Applications of evolutionary computing
  • Year:
  • 2008

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Abstract

The design of computer architectures requires the setting of multiple parameters on which the final performance depends. The number of possible combinations make an extremely huge search space. A way of setting such parameters is simulating all the architecture configurations using benchmarks. However, simulation is a slow solution since evaluating a single point of the search space can take hours. In this work we propose using artificial neural networks to predict the configurations performance instead of simulating all them. A prior model proposed by Ypek et al. [1] uses multilayer perceptron (MLP) and statistical analysis of the search space to minimize the number of training samples needed. In this paper we use evolutionary MLP and a random sampling of the space, which reduces the need to compute the performance of parameter settings in advance. Results show a high accuracy of the estimations and a simplification in the method to select the configurations we have to simulate to optimize the MLP.