@incollection{bon-men-des-22-aa-appqual,
  author = {Bonnot, Justine and M{\'e}nard, Daniel and Desnos, Karol},
  title = {Analysis of the Impact of Approximate Computing on the Application Quality},
  booktitle = {Approximate Computing Techniques},
  isbn = {978-3-030-94704-0},
  pages = {145–176},
  year = 2022,
  month = jan,
  doi = {10.1007/978-3-030-94705-7_6},
  comment = {Survey paper. Has a description of AA and of Modified Affine Arithmetic (MAA). The latter tries to keep track of the PDF of a variable by approximating the PDF (not the variable) by a bunch of affine forms on binary intervals covering its range. Discusses application of AA to noise level estimation in in signal processing due to computation errors.  Mentions the LibAffa \textt{C++} library.},
  abstract = {By exploiting the error resilience of numerous applications Approximate Computing (AC) allows saving energy or reducing the application execution time but at the expense of introducing errors in the processing. The numerical accuracy of an application is now taken as a new tunable parameter to design more efficient systems. Nevertheless, the numerical accuracy of an application has to stay within an acceptable limit to be usable. For this reason, the impact of the induced errors on the application has to be studied. AC techniques generate various error profiles. When implementing AC in an application, the objective of error analysis is to derive the impact of the induced approximations on the application quality metric. The evaluation of the impact of the approximation on the application quality metric can be done in three steps. The first step corresponds to the AC error characterization which aims at developing a model defining the error due to a specific AC technique. In this chapter, the two types of techniques used to characterize the AC error metrics are described. Analytical approaches aim at defining a mathematical model of the error metrics. Simulation-based techniques integrate emulation techniques in the application source code to mimic the AC error behavior. The second and third steps aims at propagating the error inside the application to determine, respectively, an accuracy metric or directly the quality metric. Like for the first step, the available analytical and simulation-based techniques are described in this chapter.}
}