Analysis of the O-GEometric History Length Branch Predictor

In this paper, we introduce and analyze the Optimized GEometric History Length (O-GEHL) branch Predictor that efficiently exploits very long global histories in the100-200 bits range. The GEHL predictor features several predictor tables T(i) (e.g. 8) indexed through independent functions of the glob...

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Bibliographic Details
Published in:32nd International Symposium on Computer Architecture (ISCA'05) pp. 394 - 405
Main Author: Seznec, Andre
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 01.05.2005
IEEE
Series:ACM Conferences
Subjects:
Accuracy
Application software
Best practices
Computer architecture
Computer systems organization > Architectures > Other architectures > Neural networks
Counting circuits
Fitting
Hardware > Electronic design automation > High-level and register-transfer level synthesis > Datapath optimization
Hardware > Electronic design automation > Logic synthesis > Circuit optimization
Hardware > Hardware validation > Functional verification > Simulation and emulation
Hardware > Very large scale integration design > Application-specific VLSI designs > Application specific processors
History
Mathematics of computing > Discrete mathematics > Graph theory > Paths and connectivity problems
Performance gain
Pipelines
Space exploration
ISBN:076952270X, 9780769522708
ISSN:1063-6897
Online Access:Get full text
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Summary:In this paper, we introduce and analyze the Optimized GEometric History Length (O-GEHL) branch Predictor that efficiently exploits very long global histories in the100-200 bits range. The GEHL predictor features several predictor tables T(i) (e.g. 8) indexed through independent functions of the global branch history and branch address. The set of used global history lengths forms a geometric series, i.e., L(j) = \alpha ^{i - 1} L(1).This allows the GEHL predictor to efficiently capture correlation on recent branch outcomes as well as on very old branches. As on perceptron predictors, the prediction is computed through the addition of the predictions read on the predictor tables. The O-GEHL predictor further improves the ability of the GEHL predictor to exploit very long histories through the addition of dynamic history fitting and dynamic threshold fitting. The O-GEHL predictor can be ahead pipelined to provide in time predictions on every cycle.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:076952270X
9780769522708
ISSN:1063-6897
DOI:10.1109/ISCA.2005.13