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.
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© 2014 Springer International Publishing Switzerland
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Scicluna, N., Bouganis, CS. (2014). FPGA-Based Parallel DBSCAN Architecture. In: Goehringer, D., Santambrogio, M.D., Cardoso, J.M.P., Bertels, K. (eds) Reconfigurable Computing: Architectures, Tools, and Applications. ARC 2014. Lecture Notes in Computer Science, vol 8405. Springer, Cham. https://doi.org/10.1007/978-3-319-05960-0_1
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DOI: https://doi.org/10.1007/978-3-319-05960-0_1
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05959-4
Online ISBN: 978-3-319-05960-0
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