Talks and Poster Presentations (with Proceedings-Entry):

M. Ayub, O. Hasan, M. Shafique:
"Statistical Error Analysis for Low Power Approximate Adders";
Talk: 2017 ACM/EDAC/IEEE 54th Design Automation Conference (DAC'17), Austin, Texas, USA; 2017-06-18 - 2017-06-22; in: "Proceedings of the 54th Annual Design Automation Conference (DAC) 2017", ACM, (2017), ISBN: 978-1-4503-4927-7; 75:1 - 75:6.

English abstract:
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.

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

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.