Clustered indexing for branch predictors

Desmet, Veerle; Vandierendonck, Hans; Bosschere, Koen De

doi:10.1016/j.micpro.2006.08.003

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…From the energy perspective, our technique saves more energy in the instruction cache (data and tag arrays) compared to the previous technique. When the proposed technique is combined with more accurate branch predictor such as [5] is adoped, we can have less than 0.07% performance degradation and less energy consumption.…”

Section: Resultsmentioning

confidence: 99%

An Accurate and Energy-Efficient Way Determination Technique for Instruction Caches by Early Tab Matching

Chung

Kim

Chung

2008

4th IEEE International Symposium on Electronic Design, Test and Applications (Delta 2008)

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This paper proposes an accurate and energy-efficient way determination (instead of prediction) technique for reducing energy consumption in the instruction cache by using early tag matching. Way prediction has been considered as one of the most efficient techniques to reduce energy consumption in the caches. The proposed scheme allows early tag matching for accurate way determination. With this feature, our scheme drastically improves the way determination accuracy compared to the previous way prediction techniques. To enable the early tag matching, the tag lookup stage is inserted prior to the fetch stage in the pipeline architecture. The tag matching is performed during the tag lookup stage, and then only one way is accessed during the fetch stage, leading to good energy efficiency. Simulation results show that the proposed technique reduces the energy consumption in the instruction cache by 55.1% on average. Moreover, our technique guarantees negligible performance degradation by overlapping two pipeline stages in case of branch misprediction.

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Section: Resultsmentioning

confidence: 99%

An Accurate and Energy-Efficient Way Determination Technique for Instruction Caches by Early Tab Matching

Chung

Kim

Chung

2008

4th IEEE International Symposium on Electronic Design, Test and Applications (Delta 2008)

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“…This mechanism partitions the PHT into clusters (groups) where each group contains branches with specific behavior. Using clustering mechanism makes Gshare predictor more accurate [6]. Partitioning mechanism also reduces interference in the PHT that is happen when more than one branch compete for the same entry in the PHT.…”

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confidence: 99%

Comparative Study on Behavior-Based Dynamic Branch Prediction using Machine Learning

Abudalfa¹,

Al-Mouhamed²,

Ahmed³

2019

IJCDS

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Modern processors fetch and execute instructions speculatively based on the outcome of branch prediction for decreasing effect of control hazards. Many branch predictors are proposed in literature to increase accuracy of the branch prediction. Some ones use machine learning technique for improving accuracy of predicting conditional branches. In this paper, we investigate this issue by evaluating different branch predictors through using a well-designed set of correlation patterns. We built a framework for testing performance of different branch predictors. Our framework demonstrates efficiency of using machine learning in predicting conditional branches. This framework is designed for mimicking various behaviors of branch predictions and can be used easily by scholars to check performance of more branch predictors. Experimental results shown in this work illustrate performance of applying different approaches proposed for predicting conditional branches in comparison with employing machine learning technique. Our findings illustrate that using machine learning provides competitive results. However, employing machine learning does not help in predicting all behaviors of conditional branches.

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Clustered indexing for branch predictors

Cited by 2 publications

References 15 publications

An Accurate and Energy-Efficient Way Determination Technique for Instruction Caches by Early Tab Matching

An Accurate and Energy-Efficient Way Determination Technique for Instruction Caches by Early Tab Matching

Comparative Study on Behavior-Based Dynamic Branch Prediction using Machine Learning

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