A Fast and Scalable FPGA-Based Parallel Processing Architecture for K-Means Clustering for Big Data Analysis

The exponential growth of complex, heterogeneous, dynamic, and unbounded data, generated by a variety of fields including health, genomics, physics, climatology, and social networks pose significant challenges in data processing and desired speed-performance. Existing processor-based software-only algorithms are incapable of analyzing and processing this enormous amount of data, efficiently and effectively. Consequently, some kind of hardware support is desirable to overcome the challenges in analyzing big data. Big data analytics involves many important data mining tasks including clustering, which categorizes the data into meaningful groups based on the similarity or dissimilarity among objects. In this research work, we introduce an efficient FPGA-based parallel processing architecture for K-means Clustering, one of the most popular clustering algorithms. Experiments are performed on a benchmark dataset to evaluate the feasibility and efficiency of our hardware design. Our hardware architecture is generic, parameterized, and scalable to support larger and varying datasets as well as a varying number of clusters. Our hardware configuration with 32 processing elements (PEs) achieved 368 times speedup compared to its software counterpart.