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Articles

CJPLS: VOL. 12, NO. 2, DECEMBER 2024 (Ongoing)

Pipeline Conflict in Processors: An Approach for Examining and Resolving Pipeline Conflict Using Machine Learning

Submitted
October 22, 2024
Published
2024-11-22

Abstract

This study explores the impact of pipeline conflicts on processor reliability and performance, focusing specifically on data hazards, one of three primary types of pipeline conflicts (the others being control hazards and structural conflicts). Data hazards arise from dependencies between instructions, causing stalls that reduce pipeline efficiency. The research applies machine learning to detect and mitigate these conflicts, using a dataset of artificial instruction sequences, each labeled as either conflict-free or containing one of three data hazard types: Read After Write (RAW), Write After Read (WAR), or Write After Write (WAW). Two machine learning models—logistic regression and Support Vector Machine (SVM)-were evaluated for their effectiveness in identifying pipeline conflicts. The logistic regression model achieved 96% accuracy and high precision, recall, and F1-scores across all categories, indicating its strong ability to accurately classify pipeline conflicts. In contrast, the SVM model achieved lower accuracy (83%) and performed inconsistently across classes, excelling in some but struggling with others, suggesting difficulties in recognizing certain conflict patterns.

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