An Energy-Efficient K-means Clustering FPGA Accelerator via Most-Significant Digit First Arithmetic

Abstract

K-means clustering is the most well-known unsupervised learning method that partitions the input dataset into $K$ clusters based on the similarity between the data samples. In this paper, to achieve an energy-efficient implementation without sacrificing performance, we take advantage of massive parallelism and digit-level pipelining via FPGA and the most-significant digit first arithmetic. Having the result of the most-significant digits in advance provides the possibility of early termination for unnecessary computations and fetching just the required most-significant part of data points from memory. This early termination technique significantly increases performance and decreases energy consumption. Our experimental results from various datasets and comparisons with the state-of-the-art FPGA accelerators indicate that our proposed design has effectively reduced energy consumption without any performance loss.