Approximate Computing
Approximate Computing is a computational paradigm that strategically relaxes the requirement for full precision in data processing to achieve significant gains in energy efficiency, performance, or resource utilization by exploiting the inherent tolerance of certain applications to output inaccuracies.
612
Publications
32.5K
Citations
1.4K
Authors
399
Institutions
Publications per year
2017–2026
392
612
Jonathan Bachrach, Huy T. Vo, Brian Richards et al. · 2012 · 905 citations
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
1–5 of 612
1.4K
20
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19
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Purdue University West Lafayette, United States
16 publications · 1.2K citations
16
h-index
16
h-index
16
h-index
1–5 of 1.4K
399
Purdue University West Lafayette
West Lafayette, United States
22 h-index
81 publications
9.1K citations

TU Wien
Vienna, Austria
18 h-index
35 publications
1.7K citations

University of Alberta
Edmonton, Canada
17 h-index
28 publications
3.4K citations
University of Illinois Urbana-Champaign
Urbana, United States
16 h-index
47 publications
2.2K citations
Microsoft (United States)
Redmond, United States
15 h-index
19 publications
2.9K citations
Karlsruhe Institute of Technology
Karlsruhe, Germany
15 h-index
44 publications
2.2K citations

Centre National de la Recherche Scientifique
Paris, France
14 h-index
53 publications
1.3K citations
Northeastern University
Boston, United States
13 h-index
18 publications
2K citations
1–8 of 399
22 publications · 1.5K citations
20
h-index
14 publications · 1K citations
15
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14 publications · 601 citations
14
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12 publications · 1.1K citations
12
h-index
11 publications · 392 citations
11
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