Abstract
This paper evaluates the contribution of various microprocessor architectural features on the execution of 4 neural networks used for classification problems. In this study, we selected the grnn, pnn, mnn and rbfn networks trained for the Iris data set and simulated with 10,000 elements datasets. Using a superscalar simulator we evaluated various architectural parameters such as IPC, memory hierarchy, branch prediction, functionnal units configuration. The main contribution of this work is to show that neural network workloads deserve their own characterization which cannot be derived from SPEC95 characteristics.
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Hammami, O. (1999). Neural network classifiers execution on superscalar microprocessors. In: Polychronopoulos, C., Fukuda, K.J.A., Tomita, S. (eds) High Performance Computing. ISHPC 1999. Lecture Notes in Computer Science, vol 1615. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0094910
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DOI: https://doi.org/10.1007/BFb0094910
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