Approximate Computing For Power Reduction
Approximate Computing for Power Reduction is a computational methodology that strategically trades off computational accuracy for reduced power consumption by exploiting error resilience in applications to achieve energy efficiency.
363
Publications
20.8K
Citations
801
Authors
252
Institutions
Publications per year
2017–2026
258
Parents
363
Neural Acceleration for General-Purpose Approximate Programs
Hadi Esmaeilzadeh, Adrian Sampson, Luís Ceze et al. · 2012 · 624 citations
Design and Analysis of Approximate Compressors for Multiplication
Amir Momeni, Jie Han, Paolo Montuschi et al. · IEEE Transactions on Computers · 2014 · 620 citations · Full text
Adrian Sampson, Werner Dietl, Emily Fortuna et al. · ACM SIGPLAN Notices · 2011 · 590 citations
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Nanjing University of Aeronautics and Astronautics, China
13 publications · 699 citations
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252
Purdue University West Lafayette
West Lafayette, United States
18 h-index
70 publications
9.3K citations

TU Wien
Vienna, Austria
15 h-index
25 publications
1.4K citations

University of Alberta
Edmonton, Canada
14 h-index
21 publications
2.8K citations
Northeastern University
Boston, United States
13 h-index
17 publications
2.2K citations
Karlsruhe Institute of Technology
Karlsruhe, Germany
11 h-index
28 publications
1.4K citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
11 h-index
31 publications
1.5K citations
University of California San Diego
San Diego, United States
10 h-index
26 publications
982 citations
Tsinghua University
Beijing, China
10 h-index
31 publications
1.4K citations
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20 publications · 1.5K citations
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10 publications · 816 citations
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12 publications · 370 citations
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10 publications · 1.2K citations
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ITETBT · 9 publications · 507 citations
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