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FPGA-Based Parallel DBSCAN Architecture

  • Neil Scicluna
  • Christos-Savvas Bouganis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8405)

Abstract

Clustering of a large number of data points is a computational demanding task that often needs the be accelerated in order to be useful in practice. The focus of this work is on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, which is one of the state-of-the-art clustering algorithms, targeting its acceleration using an FPGA device. The paper presents a novel, optimised and scalable architecture that takes advantage of the internal memory structure of modern FPGAs in order to deliver a high performance clustering system. Results show that the developed system can obtain average speed-ups of 32x in real-world tests and 202x in synthetic tests when compared to state-of-the-art software counterparts.

Keywords

Clustering DBSCAN FPGA Parallel Hardware Architectures 

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References

  1. 1.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters. In: Proc. of 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, pp. 226–231 (1996)Google Scholar
  2. 2.
    Daszykowski, M., Walczak, B., Massart, D.L.: Looking for Natural Patterns in Data. Part 1: Density Based Approach. Chemometrics and Intelligent Laboratory Systems 56(2), 83–92 (2001)CrossRefGoogle Scholar
  3. 3.
    Thapa, R., Trefftz, C., Wolffe, G.: Memory-Efficient Implementation of a Graphics Processor-Based Cluster Detection Algorithm for Large Spatial Databases. In: Proc. of the IEEE International Conference on Electro/Information Technology (EIT), vol. 1(5), pp. 20–22 (2010)Google Scholar
  4. 4.
    He, Y., Tan, H., Luo, W., Mao, H., Ma, D., Feng, S., Fan, J.: MR-DBSCAN: An Efficient Parallel Density-Based Clustering Algorithm Using MapReduce. In: Proc. of the IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS), vol. 7(9), pp. 473–480 (2011)Google Scholar
  5. 5.
    Hartigan, J.A., Wong, M.A.: A K-Means Clustering Algorithm. Journal of the Royal Statistical Society, Series C 28(1), 100–108 (1979)zbMATHGoogle Scholar
  6. 6.
    Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: OPTICS: Ordering Points To Identify the Clustering Structure. In: Proc. of the ACM SIGMOD International Conference on Management of Data, vol. 28(2), pp. 49–60 (1999)Google Scholar
  7. 7.
    Maruyama, T.: Real-time K-Means Clustering for Color Images on Reconfigurable Hardware. In: Proc. of 18th International Conference on Pattern Recognition (ICPR), vol. 2(1), pp. 816–819 (2006)Google Scholar
  8. 8.
    Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree: An Efficient and Robust Access Method for Points and Rectangles. In: Proc. of ACM SIGMOD Int. Conf. on Management of Data, Atlantic City, NJ, pp. 322–331 (1990)Google Scholar
  9. 9.
    Chen, M., Gao, X., Li, H.: Parallel DBSCAN with Priority R-Tree. In: Proc. of the 2nd IEEE International Conference on Information Management and Engineering (ICIME), vol. 16(18), pp. 508–511 (2010)Google Scholar
  10. 10.
    Li, L., Xi, Y.: Research on Clustering Algorithm and Its Parallelization Strategy. In: Proc. of the International Conference on Computational and Information Sciences (ICCIS), vol. 21(23), pp. 325–328 (2011)Google Scholar
  11. 11.
    Shimada, A., Zhu, H., Shibata, T.: A VLSI DBSCAN Processor Composed as an Array of Micro Agents Having Self-Growing Interconnects. In: Proc. of the IEEE International Symposium on Circuits and Systems (ISCAS), vol. 19(23), pp. 2062–2065 (2013)Google Scholar
  12. 12.
    Achtert, E., Kriegel, H.-P., Schubert, E., Zimek, A.: Interactive Data Mining with 3D-Parallel-Coordinate-Trees. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, New York, NY, pp. 1009–1012 (2013)Google Scholar
  13. 13.
    Xiang, X., Tuo, S., Pranav, V., Jaehwan, J.L.: R-tree: A Hardware Implementation. In: Proceedings of the 2008 International Conference on Computer Design (CDES), Las Vegas, NV, pp. 3–9 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Neil Scicluna
    • 1
  • Christos-Savvas Bouganis
    • 1
  1. 1.EEE DepartmentImperial College LondonLondonUnited Kingdom

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