Approximate computing in hardware and software promises significantly improved computational performance combined with very low power and energy consumption. This goal is achieved by both relaxing strict requirements on accuracy and precision, and by allowing a deviating behavior from exact Boolean specifications to a certain extent. Today, approximate computing is often limited to applications with a certain degree of inherent error tolerance, where perfect computational results are not always required. However, in order to fully utilize its benefits, the scope of applications has to be significantly extended to other compute-intensive domains including science and engineering. To meet the often rather strict quality and reliability requirements for computational results in these domains, the use of appropriate characterization and fault tolerance measures is highly required. In this paper, we evaluate some of the available techniques and how they may extend the scope of application for approximate computing.
%0 Conference Paper
%1 WundeBS2016
%A Wunderlich, Hans-Joachim
%A Braun, Claus
%A Schöll, Alexander
%B Proceedings of the 22nd IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS'16)
%D 2016
%K AxC SimTech approximate characterization computing fault metrics myown precision tolerance variable
%P 133--136
%R http://dx.doi.org/10.1109/IOLTS.2016.7604686
%T Pushing the Limits: How Fault Tolerance Extends the Scope of Approximate Computing
%X Approximate computing in hardware and software promises significantly improved computational performance combined with very low power and energy consumption. This goal is achieved by both relaxing strict requirements on accuracy and precision, and by allowing a deviating behavior from exact Boolean specifications to a certain extent. Today, approximate computing is often limited to applications with a certain degree of inherent error tolerance, where perfect computational results are not always required. However, in order to fully utilize its benefits, the scope of applications has to be significantly extended to other compute-intensive domains including science and engineering. To meet the often rather strict quality and reliability requirements for computational results in these domains, the use of appropriate characterization and fault tolerance measures is highly required. In this paper, we evaluate some of the available techniques and how they may extend the scope of application for approximate computing.
@inproceedings{WundeBS2016,
abstract = {Approximate computing in hardware and software promises significantly improved computational performance combined with very low power and energy consumption. This goal is achieved by both relaxing strict requirements on accuracy and precision, and by allowing a deviating behavior from exact Boolean specifications to a certain extent. Today, approximate computing is often limited to applications with a certain degree of inherent error tolerance, where perfect computational results are not always required. However, in order to fully utilize its benefits, the scope of applications has to be significantly extended to other compute-intensive domains including science and engineering. To meet the often rather strict quality and reliability requirements for computational results in these domains, the use of appropriate characterization and fault tolerance measures is highly required. In this paper, we evaluate some of the available techniques and how they may extend the scope of application for approximate computing.},
added-at = {2018-03-19T16:15:07.000+0100},
author = {Wunderlich, Hans-Joachim and Braun, Claus and Schöll, Alexander},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/28a8906e8a66690ce05e59dd8e68e839c/clausbraun},
booktitle = {Proceedings of the 22nd IEEE International Symposium on On-Line Testing and Robust System Design (IOLTS'16)},
doi = {http://dx.doi.org/10.1109/IOLTS.2016.7604686},
file = {http://www.iti.uni-stuttgart.de/fileadmin/rami/files/publications/2016/IOLTS_WundeBS2016.pdf},
interhash = {c3a518fb3206211e0d7da07a36661164},
intrahash = {8a8906e8a66690ce05e59dd8e68e839c},
keywords = {AxC SimTech approximate characterization computing fault metrics myown precision tolerance variable},
pages = {133--136},
timestamp = {2018-03-19T15:26:57.000+0100},
title = {{Pushing the Limits: How Fault Tolerance Extends the Scope of Approximate Computing}},
year = 2016
}
