Data Dependency Analysis
Data Dependency Analysis is a methodological construct in computer architecture that identifies and evaluates temporal relationships between instructions in a superscalar processor to determine whether one instruction must execute before another due to shared data elements, thereby enabling efficient instruction-level parallelism while preserving program correctness.
238
Publications
10.8K
Citations
620
Authors
228
Institutions
Publications per year
2017–2026
38
238
PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning
Linghao Song, Xuehai Qian, Hai Li et al. · 2017 · 821 citations
Conversion of control dependence to data dependence
John R. Allen, Ken Kennedy, Carrie Porterfield et al. · 1983 · 605 citations
Dynamic speculation and synchronization of data dependences
Andreas Moshovos, Scott E. Breach, T. N. Vijaykumar et al. · 1997 · 291 citations · Full text
Highly accurate data value prediction using hybrid predictors
Kai Wang, Manoj Franklin · 1997 · 277 citations
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228
Georgia Institute of Technology
Atlanta, United States
8 h-index
20 publications
602 citations
Carnegie Mellon University
Pittsburgh, United States
8 h-index
19 publications
841 citations
University of Illinois Urbana-Champaign
Urbana, United States
7 h-index
12 publications
405 citations
Technion – Israel Institute of Technology
Haifa, Israel
6 h-index
11 publications
510 citations

Rice University
Houston, United States
6 h-index
12 publications
2.9K citations
Nanyang Technological University
Singapore, Singapore
4 h-index
9 publications
325 citations
Tsinghua University
Beijing, China
4 h-index
14 publications
375 citations
University of California San Diego
San Diego, United States
4 h-index
10 publications
873 citations
1–8 of 228
6 publications · 120 citations
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TPDS · 6 publications · 399 citations
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5 publications · 118 citations
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4 publications · 85 citations
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Repository · 3 publications · 70 citations
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