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A Survey Of Techniques for Approximate Computing

Sparsh Mittal
Oak Ridge National Laboratory (ORNL)
ACM Computing Surveys, 2016

@article{mittal2016actSurvey,

   title={A Survey Of Techniques for Approximate Computing},

   year={2016},

   author={Sparsh Mittal},

   journal={ACM Computing Surveys},

   url={https://www.academia.edu/20201007/A_Survey_Of_Techniques_for_Approximate_Computing},

   urllink={https://www.researchgate.net/publication/290194892_A_Survey_Of_Techniques_for_Approximate_Computing}

}

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Approximate computing trades off computation quality with the effort expended and as rising performance demands confront with plateauing resource budgets, approximate computing has become, not merely attractive, but even imperative. In this paper, we present a survey of techniques for approximate computing (AC). We discuss strategies for finding approximable program portions and monitoring output quality, techniques for using AC in different processing units (e.g., CPU, GPU and FPGA), processor components, memory technologies etc., and programming frameworks for AC. We classify these techniques based on several key characteristics to emphasize their similarities and differences. The aim of this paper is to provide insights to researchers into working of AC techniques and inspire more efforts in this area to make AC the mainstream computing approach in future systems.
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