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==Application areas==
Approximate computing has been used in a variety of domains where the applications are error-tolerant, such as [[multimedia]] processing, [[machine learning]], [[signal processing]], [[Computational science|scientific computing]]. Therefore, approximate computing is mostly driven by applications that are related to human perception/cognition and have inherent error resilience. Many of these applications are based on statistical or probabilistic computation, such as different approximations can be made to better suit the desired objectives.<ref>{{cite journal |last1=Liu |first1=Weiqiang |last2=Lombardi |first2=Fabrizio |last3=Schulte |first3=Michael |title=Approximate Computing: From Circuits to Applications |journal=Proceedings of the IEEE |date=Dec 2020 |volume=108 |issue=12 |page=2103 |doi=10.1109/JPROC.2020.3033361 |access-date=26 May 2021 | url=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9264836|doi-access=free }}</ref>
One notable application in [[machine learning]] is that Google is using this approach in their [[Tensor processing unit]]s (TPU, a custom ASIC). <ref>{{cite journal |last1=Liu |first1=Weiqiang |last2=Lombardi |first2=Fabrizio |last3=Schulte |first3=Michael |title=Approximate Computing: From Circuits to Applications |journal=Proceedings of the IEEE |date=Dec 2020 |volume=108 |issue=12 |page=2104 |doi=10.1109/JPROC.2020.3033361 |access-date=26 May 2021 | url=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9264836|doi-access=free }}</ref>
==Derived paradigms==
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