New directions in computing and algorithms has lead to some new applications that have tolerance to imprecision. Although, These applications are creating large volumes of data which exceeds the capability of today’s computing systems. Therefore, researchers are trying to find new techniques to alleviate this crisis. Approximate Computing is one promising technique that uses a trade off between...

Emerging applications such as R(ecognition), M(ining), and S(ynthesis) suit themselves well to approximate computing due to their intrinsic noise tolerance. RMS applications process massive, yet noisy and redundant data by probabilistic, often iterative, algorithms. Usually the solution space has many more elements than one, rendering a range of application outputs valid, as opposed to a single...

This paper presents approximate confidence intervals for each function of parameters in a Banach space based on a bootstrap algorithm. We apply kernel density approach to estimate the persistence landscape. In addition, we evaluate the quality distribution function estimator of random variables using integrated mean square error (IMSE). The results of simulation studies show a significant impro...

Approximate computing has shown to provide new ways to improve performance and power consumption of errorresilient applications. While many of these applications can be found in image processing, data classification or machine learning, we demonstrate its suitability to a problem from scientific computing. Utilizing the self-correcting behavior of iterative algorithms, we show that approximate ...

Approximate computing aims for efficient execution of workflows where an approximate output is sufficient instead of the exact output. The idea behind approximate computing is to compute over a representative sample instead of the entire input dataset. Thus, approximate computing — based on the chosen sample size — can make a systematic trade-off between the output accuracy and computation effi...

Energy efficiency is a key concern in the design of modern computer systems. One promising approach to energyefficient computation, approximate computing, trades off output precision for energy efficiency. However, this tradeoff can have unexpected effects on computation quality. This paper presents dynamic-analysis tools to debug and monitor the quality of approximate computations. We propose ...