About: Approximate computing

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Approximate computing is an emerging paradigm for energy-efficient and/or high-performance design. It includes a plethora of computation techniques that return a possibly inaccurate result rather than a guaranteed accurate result, and that can be used for applications where an approximate result is sufficient for its purpose. One example of such situation is for a search engine where no exact answer may exist for a certain search query and hence, many answers may be acceptable. Similarly, occasional dropping of some frames in a video application can go undetected due to perceptual limitations of humans. Approximate computing is based on the observation that in many scenarios, although performing exact computation requires large amount of resources, allowing bounded approximation can provid

Attributes	Values
rdf:type	programming language
rdfs:label	Approximate computing (en)
rdfs:comment	Approximate computing is an emerging paradigm for energy-efficient and/or high-performance design. It includes a plethora of computation techniques that return a possibly inaccurate result rather than a guaranteed accurate result, and that can be used for applications where an approximate result is sufficient for its purpose. One example of such situation is for a search engine where no exact answer may exist for a certain search query and hence, many answers may be acceptable. Similarly, occasional dropping of some frames in a video application can go undetected due to perceptual limitations of humans. Approximate computing is based on the observation that in many scenarios, although performing exact computation requires large amount of resources, allowing bounded approximation can provid (en)
dbp:wikiPageUsesTemplate	dbt:Clarify dbt:Reflist dbt:Short_description
Subject	Approximations Computer architecture Software optimization
gold:hypernym	Computation
prov:wasDerivedFrom	http://en.wikipedia.org/wiki/Approximate_computing?oldid=1056927318&ns=0
Wikipage page ID	49277634 (xsd:integer)
page length (characters) of wiki page	11086 (xsd:nonNegativeInteger)
Wikipage revision ID	1056927318 (xsd:integer)
Link from a Wikipage to another Wikipage	Floating-point arithmetic Logic gate Machine learning Magnetoresistive RAM Program optimization Programmer Programming model Adder (electronics) Approximation theory Artificial neural network Binary multiplier Monte Carlo algorithm Static random-access memory Task skipping Approximations Computer architecture Software optimization Metaheuristic PCMOS Crash (computing) K-means clustering Tensor Processing Unit Computational science Computer data storage Control flow Static random-access memory EDRAM Data truncation Error detection and correction Film frame Subject-matter expert Iteration Loop perforation Memoization Mixed media Randomized algorithm Refresh rate Signal processing
has abstract	Approximate computing is an emerging paradigm for energy-efficient and/or high-performance design. It includes a plethora of computation techniques that return a possibly inaccurate result rather than a guaranteed accurate result, and that can be used for applications where an approximate result is sufficient for its purpose. One example of such situation is for a search engine where no exact answer may exist for a certain search query and hence, many answers may be acceptable. Similarly, occasional dropping of some frames in a video application can go undetected due to perceptual limitations of humans. Approximate computing is based on the observation that in many scenarios, although performing exact computation requires large amount of resources, allowing bounded approximation can provide disproportionate gains in performance and energy, while still achieving acceptable result accuracy. For example, in k-means clustering algorithm, allowing only 5% loss in classification accuracy can provide 50 times energy saving compared to the fully accurate classification. The key requirement in approximate computing is that approximation can be introduced only in non-critical data, since approximating critical data (e.g., control operations) can lead to disastrous consequences, such as program crash or erroneous output. (en)
foaf:isPrimaryTopicOf	http://en.wikipedia.org/wiki/Approximate_computing
is Link from a Wikipage to another Wikipage of	Machine learning NP-hardness List of genetic algorithm applications IEEE Rebooting Computing Loop perforation Memoization Memory refresh
is foaf:primaryTopic of	http://en.wikipedia.org/wiki/Approximate_computing

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