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Neural network classifiers execution on superscalar microprocessors

  • Omar Hammami
II System Architecture
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1615)

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.

Key words

classification microarchitecture neural network performance superscalar 

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Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Omar Hammami
    • 1
  1. 1.University of AizuFukushimaJapan

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