research-article

ARC 2014: A Multidimensional FPGA-Based Parallel DBSCAN Architecture

Authors:

Christos-Savvas BouganisAuthors Info & Claims

ACM Transactions on Reconfigurable Technology and Systems (TRETS), Volume 9, Issue 1

Article No.: 2, Pages 1 - 15

https://doi.org/10.1145/2724722

Published: 04 November 2015 Publication History

Abstract

Clustering large numbers of data points is a very computationally demanding task that often needs to be accelerated in order to be useful in practical applications. This work focuses on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which is one of the state-of-the-art clustering algorithms, and targets its acceleration using an FPGA device. The article presents an optimized, scalable, and parameterizable architecture that takes advantage of the internal memory structure of modern FPGAs in order to deliver a high-performance clustering system. Post-synthesis simulation results show that the developed system can obtain mean speedups of 31× in real-world tests and 202× in synthetic tests when compared to state-of-the-art software counterparts running on a quad-core 3.4GHz Intel i7-2600k. Additionally, this implementation is also capable of clustering data with any number of dimensions without impacting the performance.

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Cited By

Lee SAn SKim JNamkung HPark JKim RLee S(2024)Grid-Based DBSCAN Clustering Accelerator for LiDAR’s Point CloudElectronics10.3390/electronics1317339513:17(3395)Online publication date: 26-Aug-2024
https://doi.org/10.3390/electronics13173395
Pinge SXu WKang JZhang TMoshiri NBittremieux WRosing T(2024)SpecHD: Hyperdimensional Computing Framework for FPGA-Based Mass Spectrometry Clustering2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546776(1-6)Online publication date: 25-Mar-2024
https://doi.org/10.23919/DATE58400.2024.10546776
Bailey D(2023)Machine LearningDesign for Embedded Image Processing on FPGAs10.1002/9781119819820.ch14(403-439)Online publication date: 5-Sep-2023
https://doi.org/10.1002/9781119819820.ch14
Show More Cited By

Index Terms

ARC 2014: A Multidimensional FPGA-Based Parallel DBSCAN Architecture
1. Hardware
  1. Very large scale integration design
    1. Application-specific VLSI designs
      1. Application specific processors

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Published In

cover image ACM Transactions on Reconfigurable Technology and Systems

ACM Transactions on Reconfigurable Technology and Systems Volume 9, Issue 1

Special Section on the 2014 International Symposium on Applied Reconfigurable Computing

November 2015

121 pages

ISSN:1936-7406

EISSN:1936-7414

DOI:10.1145/2839314

Editor:
Steve Wilton
Department of Electrical and Computer Engineering/University of British Columbia/Kaiser 4112, 5500-2332 Main Mall/Vancouver, BC V6T 1Z4 Canada

Issue’s Table of Contents

Copyright © 2015 Owner/Author.

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.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 November 2015

Accepted: 01 January 2015

Revised: 01 November 2014

Received: 01 June 2014

Published in TRETS Volume 9, Issue 1

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Cited By

Lee SAn SKim JNamkung HPark JKim RLee S(2024)Grid-Based DBSCAN Clustering Accelerator for LiDAR’s Point CloudElectronics10.3390/electronics1317339513:17(3395)Online publication date: 26-Aug-2024
https://doi.org/10.3390/electronics13173395
Pinge SXu WKang JZhang TMoshiri NBittremieux WRosing T(2024)SpecHD: Hyperdimensional Computing Framework for FPGA-Based Mass Spectrometry Clustering2024 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE58400.2024.10546776(1-6)Online publication date: 25-Mar-2024
https://doi.org/10.23919/DATE58400.2024.10546776
Bailey D(2023)Machine LearningDesign for Embedded Image Processing on FPGAs10.1002/9781119819820.ch14(403-439)Online publication date: 5-Sep-2023
https://doi.org/10.1002/9781119819820.ch14
Sarpanah ZMosavi MShafiee E(2022)GPS Receivers Spoofing Detection Based on Subtractive, FCM and DBSCAN Clustering AlgorithmsJournal of Circuits, Systems and Computers10.1142/S021812662350152932:09Online publication date: 8-Dec-2022
https://doi.org/10.1142/S0218126623501529
Dafir ZLamari YSlaoui S(2021)A survey on parallel clustering algorithms for Big DataArtificial Intelligence Review10.1007/s10462-020-09918-254:4(2411-2443)Online publication date: 1-Apr-2021
https://dl.acm.org/doi/10.1007/s10462-020-09918-2
Cai ZWang JHe K(2020)Adaptive Density-Based Spatial Clustering for Massive Data AnalysisIEEE Access10.1109/ACCESS.2020.29694408(23346-23358)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2969440

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