TY - GEN
T1 - Statistical Error Analysis for Low Power Approximate Adders
AU - Ayub, Muhammad Kamran
AU - Hasan, Osman
AU - Shafique, Muhammad
N1 - Publisher Copyright:
© 2017 ACM.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2017/6/18
Y1 - 2017/6/18
N2 - 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.
AB - 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.
KW - Accuracy
KW - Approximate Computing
KW - Error
KW - Low Power
KW - Performance
KW - Probabilistic Analysis
KW - Scalability
UR - http://www.scopus.com/inward/record.url?scp=85023623760&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85023623760&partnerID=8YFLogxK
U2 - 10.1145/3061639.3062319
DO - 10.1145/3061639.3062319
M3 - Conference contribution
AN - SCOPUS:85023623760
T3 - Proceedings - Design Automation Conference
BT - Proceedings of the 54th Annual Design Automation Conference 2017, DAC 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 54th Annual Design Automation Conference, DAC 2017
Y2 - 18 June 2017 through 22 June 2017
ER -