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K-means is a popular but computation-intensive algorithm for unsupervisedlearning. To address this issue, we present KPynq, a work-efficienttriangle-inequality based K-means on FPGA for handling large-size,high-dimension datasets. KPynq leverages an algorithm-level optimization tobalance the performance and computation irregularity, and a hardwarearchitecture design to fully exploit the pipeline and parallel processingcapability of various FPGAs. In the experiment, KPynq consistently outperformsthe CPU-based standard K-means in terms of its speedup (up to 4.2x) andsignificant energy-efficiency (up to 218x).

KPynq: A Work-Efficient Triangle-Inequality based K-means on FPGA