Architecture performance prediction using evolutionary artificial neural networks

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. © 2008 Springer-Verlag Berlin Heidelberg.