Parallel Inference
Parallel Inference is a scalable machine learning method that enables the simultaneous execution of multiple inference tasks across distributed computing resources to reduce overall latency and improve throughput.
169
Publications
8.2K
Citations
588
Authors
201
Institutions
Publications per year
2017–2026
67
Parents
169
GraphLab: A New Framework For Parallel Machine Learning
Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola et al. · arXiv (Cornell University) · 2014 · 441 citations · Full text
More is Less: A More Complicated Network with Less Inference Complexity
Xuanyi Dong, Junshi Huang, Yi Yang et al. · 2017 · 296 citations
GraphLab: A New Framework for Parallel Machine Learning
Yucheng Low, Joseph E. Gonzalez, Aapo Kyrola et al. · arXiv (Cornell University) · 2010 · 258 citations · Full text
TrueNorth: Accelerating From Zero to 64 Million Neurons in 10 Years
Michael DeBole, Brian Taba, Arnon Amir et al. · Computer · 2019 · 236 citations
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201
Carnegie Mellon University
Pittsburgh, United States
9 h-index
26 publications
4.7K citations
ETH Zurich
Zurich, Switzerland
8 h-index
14 publications
981 citations
University of Southern California
Los Angeles, United States
8 h-index
20 publications
650 citations
University of California, Berkeley
Berkeley, United States
6 h-index
25 publications
1.6K citations
Seoul National University
Seoul, South Korea
5 h-index
11 publications
642 citations
Duke University
Durham, United States
4 h-index
13 publications
643 citations
Stanford University
Stanford, United States
4 h-index
13 publications
708 citations
National University of Singapore
Singapore, Singapore
3 h-index
7 publications
599 citations
1–8 of 201
Repository · 24 publications · 1.8K citations
18
h-index
4 publications · 301 citations
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FGCS · 4 publications · 80 citations
4
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3 publications · 67 citations
3
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Repository · 3 publications · 168 citations
3
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