Conference Paper

Architecture Performance Prediction Using Evolutionary Artificial Neural Networks

DOI: 10.1007/978-3-540-78761-7_18 Conference: Applications of Evolutionary Computing, EvoWorkshops 2008: EvoCOMNET, EvoFIN, EvoHOT, EvoIASP, EvoMUSART, EvoNUM, EvoSTOC, and EvoTransLog, Naples, Italy, March 26-28, 2008. Proceedings
Source: DBLP


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.

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