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EVOW
2008
Springer

Architecture Performance Prediction Using Evolutionary Artificial Neural Networks

8 years 12 months ago
Architecture Performance Prediction Using Evolutionary Artificial Neural Networks
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 opt...
Pedro A. Castillo, Antonio Miguel Mora, Juan Juli&
Added 19 Oct 2010
Updated 19 Oct 2010
Type Conference
Year 2008
Where EVOW
Authors Pedro A. Castillo, Antonio Miguel Mora, Juan Julián Merelo Guervós, Juan Luís Jiménez Laredo, Miquel Moretó, Francisco J. Cazorla, Mateo Valero, Sally A. McKee
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