Two-level branch prediction using neural networks

  • Authors:

  • Colin Egan;Gordon Steven;Patrick Quick;Rubén Anguera;Fleur Steven;Lucian Vintan

  • Affiliations:

  • University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK;University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK;University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK;University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK;University of Hertfordshire, College Lane, Hatfield AL10 9AB, UK;University "Lucian Blaga" of Sibiu, Sibiu-2400, Romania

  • Venue:
  • Journal of Systems Architecture: the EUROMICRO Journal - Special issue: Synthesis and verification
  • Year:
  • 2003

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Abstract

Dynamic branch prediction in high-performance processors is a specific instance of a general time series prediction problem that occurs in many areas of science. Most branch prediction research focuses on two-level adaptive branch prediction techniques, a very specific solution to the branch prediction problem. An alternative approach is to look to other application areas and fields for novel solutions to the problem. In this paper, we examine the application of neural networks to dynamic branch prediction. We retain the first level history register of conventional two-level predictors and replace the second level PHT with a neural network. Two neural networks are considered: a learning vector quantisation network and a backpropagation network. We demonstrate that a neural predictor can achieve misprediction rates comparable to conventional two-level adaptive predictors and suggest that neural predictors merit further investigation.