K-Means clustering is a popular technique for data partitioning, frequently used in data mining. The simple control flow, and high degree of parallelism, makes it a good candidate for FPGA acceleration. We propose a highly configurable architecture, based on Euclidean distance computation. It can be tuned by the following parameters: number of dimensions, dimension width, dimension based parallelism degree, number of centroids and centroid based parallelism degree. We study their impact on different K-Means components, such as the distance computation, distance comparison, accumulation, division, or the memory modules within the accelerator. Furthermore, for the aforementioned parameters we investigate the performance/cost trade-offs of the proposed K-Means accelerator implementation.
K-Means clustering, FPGA, parallelization.
Alexandru AMARICAI, "Design Trade-offs in Configurable FPGA Architectures for K-Means Clustering", Studies in Informatics and Control, ISSN 1220-1766, vol. 26(1), pp. 43-48, 2017. https://doi.org/10.24846/v26i1y201705
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