Context-sensitive Prediction
Context-Sensitive Prediction is a branch prediction technique that, as a refinement of dynamic prediction methods, distinguishes itself by incorporating historical execution context—such as recent branch outcomes, program path information, or architectural states—beyond the immediate branch address to improve prediction accuracy.
86
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4.4K
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264
Authors
132
Institutions
Publications per year
2017–2026
26
Parents
86
The predictability of data values
Yiannakis Sazeides, James E. Smith · 1997 · 393 citations
Contextual Gaussian Process Bandit Optimization
Andreas Krause, Cheng Soon Ong · 2011 · 259 citations
The predictability of data values
Yanos Sazeides, James E. Smith · 2002 · 230 citations
The YAGS branch prediction scheme
A.N. Eden, Trevor Mudge · 1998 · 152 citations
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University of Michigan
Ann Arbor, United States
4 h-index
9 publications
570 citations
Intel (Israel)
Kiryat Gat, Israel
3 h-index
21 publications
1.1K citations
Universitat Politècnica de Catalunya
Barcelona, Spain
3 h-index
9 publications
420 citations
Friedrich-Alexander-Universität Erlangen-Nürnberg
Erlangen, Germany
3 h-index
6 publications
128 citations
University of Wisconsin–Madison
Madison, United States
3 h-index
6 publications
1.3K citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
4 publications
102 citations
North Carolina State University
Raleigh, United States
2 h-index
8 publications
308 citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
2 h-index
5 publications
148 citations
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Repository · 3 publications · 79 citations
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2 publications · 125 citations
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2 publications · 38 citations
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2 publications · 108 citations
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1 publications · 20 citations
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