Dynamic branch prediction using neural networks

G.B. Steven, R. Anguera, C. Egan, F.L. Steven, L. Vintan

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    15 Citations (Scopus)
    75 Downloads (Pure)

    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. In contrast, 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. Two neural networks are considered: a lecturing vector quantisation (LVQ) 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.
    Original languageEnglish
    Title of host publicationProcs of Euromicro Symposium on Digital Systems, Design
    PublisherInstitute of Electrical and Electronics Engineers (IEEE)
    Pages178-185
    Volume2001
    ISBN (Print)0-7695-1239-9
    DOIs
    Publication statusPublished - 2001

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