research-article

Parallel architectures for the kNN classifier -- design of soft IP cores and FPGA implementations

Authors:

Ioannis Stamoulias,

Elias S. ManolakosAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 13, Issue 2

Article No.: 22, Pages 1 - 21

https://doi.org/10.1145/2514641.2514649

Published: 30 September 2013 Publication History

Abstract

We designed a variety of k-nearest-neighbor parallel architectures for FPGAs in the form of parameterizable soft IP cores. We show that they can be used to solve large classification problems with thousands of training vectors, or thousands of vector dimensions using a single FPGA, and achieve very high throughput. They can be used to flexibly synthesize architectures that also cover: 1NN classification (vector quantization), multishot queries (with different k), LOOCV cross-validation, and compare favorably to GPU implementations. To the best of our knowledge this is the first attempt to design flexible IP cores for the popular kNN classifier.

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Cited By

Langehaug TGraham S(2024)CuMONITOR: Continuous Monitoring of Microarchitecture for Software Task Identification and ClassificationDigital Threats: Research and Practice10.1145/36528615:3(1-22)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3652861
Djosic SJovanovic MLj. Djordjevic G(2024)Optimized k-Nearest neighbors search implementation on resource-constrained FPGA platformsMicroprocessors and Microsystems10.1016/j.micpro.2024.105089109(105089)Online publication date: Sep-2024
https://doi.org/10.1016/j.micpro.2024.105089
Altman MWan WHosseini AArabi Nowdeh SAlizadeh M(2024)Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A reviewHeliyon10.1016/j.heliyon.2024.e2665210:4(e26652)Online publication date: Feb-2024
https://doi.org/10.1016/j.heliyon.2024.e26652
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Parallel architectures for the kNN classifier -- design of soft IP cores and FPGA implementations
1. Computer systems organization

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 13, Issue 2

Special issue on application-specific processors

September 2013

254 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/2514641

Issue’s Table of Contents

Copyright © 2013 ACM.

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 30 September 2013

Accepted: 01 March 2012

Revised: 01 September 2011

Received: 01 February 2011

Published in TECS Volume 13, Issue 2

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Cited By

Langehaug TGraham S(2024)CuMONITOR: Continuous Monitoring of Microarchitecture for Software Task Identification and ClassificationDigital Threats: Research and Practice10.1145/36528615:3(1-22)Online publication date: 28-Mar-2024
https://dl.acm.org/doi/10.1145/3652861
Djosic SJovanovic MLj. Djordjevic G(2024)Optimized k-Nearest neighbors search implementation on resource-constrained FPGA platformsMicroprocessors and Microsystems10.1016/j.micpro.2024.105089109(105089)Online publication date: Sep-2024
https://doi.org/10.1016/j.micpro.2024.105089
Altman MWan WHosseini AArabi Nowdeh SAlizadeh M(2024)Machine learning algorithms for FPGA Implementation in biomedical engineering applications: A reviewHeliyon10.1016/j.heliyon.2024.e2665210:4(e26652)Online publication date: Feb-2024
https://doi.org/10.1016/j.heliyon.2024.e26652
Wang YFeng BLi GDeng LXie YDing Y(2022)STPAcc: Structural TI-Based Pruning for Accelerating Distance-Related Algorithms on CPU-FPGA PlatformsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.309339441:5(1358-1370)Online publication date: May-2022
https://doi.org/10.1109/TCAD.2021.3093394
Almomany AAyyad WJarrah A(2022)Optimized implementation of an improved KNN classification algorithm using Intel FPGA platform: Covid-19 case studyJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2022.04.00634:6(3815-3827)Online publication date: Jun-2022
https://doi.org/10.1016/j.jksuci.2022.04.006
Marquez-Viloria DCastano-Londono LGuerrero-Gonzalez N(2021)A Modified KNN Algorithm for High-Performance Computing on FPGA of Real-Time m-QAM DemodulatorsElectronics10.3390/electronics1005062710:5(627)Online publication date: 9-Mar-2021
https://doi.org/10.3390/electronics10050627
Arias-Garcia JMafra AGade LCoelho FCastro CTorres LBraga A(2021)Enhancing Performance of Gabriel Graph-Based Classifiers by a Hardware Co-Processor for Embedded System ApplicationsIEEE Transactions on Industrial Informatics10.1109/TII.2020.298732917:2(1186-1196)Online publication date: Feb-2021
https://doi.org/10.1109/TII.2020.2987329
Zhou SGuo QDu ZLiu DChen TLi LLiu SZhou JTemam OFeng XZhou XChen Y(2020)ParaML: A Polyvalent Multicore Accelerator for Machine LearningIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2019.292752339:9(1764-1777)Online publication date: Sep-2020
https://doi.org/10.1109/TCAD.2019.2927523
Sun GJun S(2020)Bandwidth Efficient Near-Storage Accelerator for High-Dimensional Similarity Search2020 International Conference on Field-Programmable Technology (ICFPT)10.1109/ICFPT51103.2020.00026(129-138)Online publication date: Dec-2020
https://doi.org/10.1109/ICFPT51103.2020.00026
Vieira JDuarte RNeto H(2019)kNN-STUFF: kNN STreaming Unit for FpgasIEEE Access10.1109/ACCESS.2019.29558647(170864-170877)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2955864
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