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Low-Complexity Scalable Architectures for Parallel Computation of Similarity Measures
Joint Authors
Kanan, Awos
Gebali, Fayez
Ibrahim, Atef
Li, Kin Fun
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-05-26
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Processor array architectures have been employed, as an accelerator, to compute similarity distance found in a variety of data mining algorithms.
However, most of the proposed architectures in the existing literature are designed in an ad hoc manner without taking into consideration the size and dimensionality of the datasets.
Furthermore, data dependencies have not been analyzed, and often, only one design choice is considered for the scheduling and mapping of computational tasks.
In this work, we present a systematic methodology to design scalable and area-efficient linear (1-D) processor arrays for the computation of similarity distance matrices.
Six possible design options are obtained and analyzed in terms of area and time complexities.
The obtained architectures provide us with the flexibility to choose the one that meets hardware constraints for a specific problem size.
Comparisons with the previously reported architectures demonstrate that one of the proposed architectures achieves less area and area-delay product besides its scalability to high-dimensional data.
American Psychological Association (APA)
Kanan, Awos& Gebali, Fayez& Ibrahim, Atef& Li, Kin Fun. 2019. Low-Complexity Scalable Architectures for Parallel Computation of Similarity Measures. Scientific Programming،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1210732
Modern Language Association (MLA)
Kanan, Awos…[et al.]. Low-Complexity Scalable Architectures for Parallel Computation of Similarity Measures. Scientific Programming No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1210732
American Medical Association (AMA)
Kanan, Awos& Gebali, Fayez& Ibrahim, Atef& Li, Kin Fun. Low-Complexity Scalable Architectures for Parallel Computation of Similarity Measures. Scientific Programming. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1210732
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1210732