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Title
  • en An energy-efficient dynamic branch predictor with a two-clock-cycle naive Bayes classifier for pipelined RISC microprocessors
Creator
    • en Hida, Itaru
    • en Takamaeda-Yamazaki, Shinya
    • en Motomura, Masato
Accessrights open access
Rights
  • en Copyright ©2017 The Institute of Electronics, Information and Communication Engineers
Subject
  • Other en dynamic branch prediction
  • Other en supervised machine learning
  • Other en naive Bayes classifier
  • Other en energy-efficient microprocessor
  • Other en low-power architecture
  • Other en CMOS digital circuit
  • NDC 540
Description
  • Abstract en In this paper, we propose a Bayesian branch-prediction circuit, consisting of an instruction-feature extractor and a naive Bayes classifier (NBC), as a machine learning approach for branch prediction. A branch predictor predicts the outcome of a branch instruction by analyzing the pattern of the previous branch outcome. In other words, branch prediction can be viewed as a type of pattern recognition problem, and such problems are often solved using neural networks. A perceptron branch predictor has already been proposed as one example of a neural branch prediction architecture, which predicts the next branch outcome by using past branch history to form feature vectors. The proposed circuit is constructed by replacing the arithmetic unit (neurons) in conventional neural branch predictors with an NBC. By introducing an approximate Bayesian computation and its parallel architectures, the NBC circuit completes branch prediction within two clock cycles per instruction. This constitutes a suitable replacement for conventional branch predictors in modern pipelined reduced instruction set computing microprocessors.
Publisher ja 電子情報通信学会 en The Institute of Electronics, Information and Communication Engineers / IEICE
Date
    Issued2017
Language
  • eng
Resource Type journal article
Version Type VoR
Identifier HDL http://hdl.handle.net/2115/68659
Relation
  • isIdenticalTo DOI https://doi.org/10.1587/nolta.8.235
Journal
    • PISSN 2185-4106
      • en Nonlinear Theory and Its Applications, IEICE
      • Volume Number8 Issue Number3 Page Start235 Page End245
File
    • fulltext 8_235.pdf
    • 978.6 KB (application/pdf)
      • Issued2017
Oaidate 2023-07-26