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Scalable K-Nearest Neighbors Implementation using Distributed Embedded Systems

Published: 02 July 2024 Publication History

Abstract

The distributed embedded systems paradigm is a promising platform for high-performance embedded applications. We present a distributed algorithm and system based on cost-effective devices. The proof of concept shows how a parallelized approach leveraging a distributed embedded platform can address the computational of the Machine Learning K-Nearest Neighbors (K-NN) algorithm with large and heterogeneous datasets.

References

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L. Bozzoli et al., "EuFRATE: European FPGA Radiation-hardened Architecture for Telecommunications," 2023 Design, Automation & Test in Europe Conference & Exhibition (DATE), Antwerp, Belgium, 2023, pp. 1--6.
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O. Knodel, A. Georgi, P. Lehmann, W. E. Nagel and R. G. Spallek, "Integration of a Highly Scalable, Multi-FPGA-Based Hardware Accelerator in Common Cluster Infrastructures," 2013 42nd International Conference on Parallel Processing, Lyon, France, 2013, pp. 893--900.
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T. Cover and P. Hart, "Nearest neighbor pattern classification," in IEEE Transactions on Information Theory, vol. 13, no. 1, pp. 21--27, January 1967.
[4]
W. Zhang, X. Chen, Y. Liu, and Q. Xi, "A Distributed Storage and Computation k-Nearest Neighbor Algorithm Based Cloud-Edge Computing for Cyber-Physical-Social Systems," in IEEE Access, vol. 8, pp. 50118--50130, 2020.
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AMD. Zynq UltraScale+ Device Technical Reference Manual, UG1085, v2.4, December 2023.
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Deng, L. (2012). The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine, 29(6), 141--142.
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Blackard, Jock. (1998). Covertype. UCI Machine Learning Repository. https://doi.org/10.24432/C50K5N.

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  1. Scalable K-Nearest Neighbors Implementation using Distributed Embedded Systems

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    cover image ACM Conferences
    CF '24: Proceedings of the 21st ACM International Conference on Computing Frontiers
    May 2024
    345 pages
    ISBN:9798400705977
    DOI:10.1145/3649153
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 02 July 2024

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    Author Tags

    1. Distributed Systems
    2. Embedded Systems
    3. K-NN
    4. Machine Learning
    5. SoC

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    CF '24 Paper Acceptance Rate 33 of 105 submissions, 31%;
    Overall Acceptance Rate 273 of 785 submissions, 35%

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