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Vorträge und Posterpräsentationen (mit Tagungsband-Eintrag):

M. Ayub, O. Hasan, M. Shafique:
"Statistical Error Analysis for Low Power Approximate Adders";
Vortrag: 2017 ACM/EDAC/IEEE 54th Design Automation Conference (DAC'17), Austin, Texas, USA; 18.06.2017 - 22.06.2017; in: "Proceedings of the 54th Annual Design Automation Conference (DAC) 2017", ACM, (2017), ISBN: 978-1-4503-4927-7; S. 75:1 - 75:6.



Kurzfassung englisch:
Low-power approximate adders provide basic building blocks for approximate computing hardware that have shown remarkable energy efficiency for error-resilient applications (like image/video processing, computer vision, etc.), especially for battery-driven portable systems. In this paper, we present a novel scalable, fast yet accurate analytical method to evaluate the output error probability of multi-bit low power adders for a predetermined probability of input bits. Our method recursively computes the error probability by considering the accurate cases only, which are considerably smaller than the erroneous ones. Our method can handle the error analysis of a wider-range of adders with negligible computational overhead. To ensure its rapid adoption in industry and academia, we have open-sourced our LabVIEW and MATLAB libraries.

Schlagworte:
Low Power, Approximate Computing, Probabilistic Analysis, Error, Performance, Scalability, Accuracy


"Offizielle" elektronische Version der Publikation (entsprechend ihrem Digital Object Identifier - DOI)
http://dx.doi.org/10.1145/3061639.3062319


Erstellt aus der Publikationsdatenbank der Technischen Universität Wien.