High-performance K-means Implementation based on a Simplified Map-Reduce Architecture

Zhehao Li, Jifang Jin, Lingli Wang

The k-means algorithm is one of the most common clustering algorithms and widely used in data mining and pattern recognition. The increasing computational requirement of big data applications makes hardware acceleration for the k-means algorithm necessary. In this paper, a simplified Map-Reduce architecture is proposed to implement the k-means algorithm on an FPGA. Algorithmic segmentation, data path elaboration and automatic control are applied to optimize the architecture for high performance. In addition, high level synthesis technique is utilized to reduce development cycles and complexity. For a single iteration in the k-means algorithm, a throughput of 28.74 Gbps is achieved. The performance shows at least 3.93x speedup compared with four representative existing FPGA-based implementations and can satisfy the demand of big data applications.

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