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. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Scicluna, N., & Bouganis, C. S. (2014). FPGA-based parallel DBSCAN architecture. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8405 LNCS, pp. 1–12). Springer Verlag. https://doi.org/10.1007/978-3-319-05960-0_1
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