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AuthorBaali H.
AuthorDjelouat H.
AuthorAmira A.
AuthorBensaali F.
AuthorZhai X.
Available date2020-02-05T08:53:38Z
Publication Date2018
Publication Name2017 IEEE 17th International Conference on Ubiquitous Wireless Broadband, ICUWB 2017 - Proceedings
Publication Name17th IEEE International Conference on Ubiquitous Wireless Broadband, ICUWB 2017
ResourceScopus
ISBN9.78E+12
URIhttp://dx.doi.org/10.1109/ICUWB.2017.8251000
URIhttp://hdl.handle.net/10576/12772
AbstractAs the use of compressed sensing (CS) in internet of things (IoT) wearable nodes increases, the need for high performance and low power CS reconstruction algorithms for the battery powered IoT Edge Devices also increases. This paper describes an efficient multicore hardware implementation of the ?1 Regularized Least Squares (LS) optimization problem based on the Alternating Direction Method of Multipliers (ADMM) algorithm. The use a decomposition technique, that exploits the special matrix structure to update its inverse, significantly reduced the processing time as well as the complexity of the algorithm. The average processing time on a parallel multicore Zynq System on Chip (SoC) device has improved by a factor of 2 compared to the C++ PC software implementation which makes it suitable for real time applications. 2017 IEEE.
SponsorThis paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectADMM
Compressed Sensing
Connected Health
IoT Edge Computing
LASSO
TitleEfficient hardware implementation of the ?1-Regularized least squares for IoT edge computing
TypeConference Paper
Pagination1-May
Volume Number2018-January


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