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An energy-efficient dynamic branch predictor with a two-clock-cycle naive Bayes classifier for pipelined RISC microprocessors

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Title: An energy-efficient dynamic branch predictor with a two-clock-cycle naive Bayes classifier for pipelined RISC microprocessors
Authors: Hida, Itaru Browse this author
Takamaeda-Yamazaki, Shinya Browse this author
Ikebe, Masayuki Browse this author →KAKEN DB
Motomura, Masato Browse this author
Asai, Tetsuya Browse this author →KAKEN DB
Keywords: dynamic branch prediction
supervised machine learning
naive Bayes classifier
energy-efficient microprocessor
low-power architecture
CMOS digital circuit
Issue Date: 2017
Publisher: 電子情報通信学会
Journal Title: Nonlinear Theory and Its Applications, IEICE
Volume: 8
Issue: 3
Start Page: 235
End Page: 245
Publisher DOI: 10.1587/nolta.8.235
Abstract: 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.
Rights: Copyright ©2017 The Institute of Electronics, Information and Communication Engineers
Type: article
URI: http://hdl.handle.net/2115/68659
Appears in Collections:情報科学院・情報科学研究院 (Graduate School of Information Science and Technology / Faculty of Information Science and Technology) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

Submitter: 肥田 格

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