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.
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Castillo, P.A. et al. (2008). Architecture Performance Prediction Using Evolutionary Artificial Neural Networks. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2008. Lecture Notes in Computer Science, vol 4974. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78761-7_18
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DOI: https://doi.org/10.1007/978-3-540-78761-7_18
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