Acceleration of k-Means Algorithm Using Altera SDK for OpenCL
Qing Tang, Mohammed A. S. Khalid · ACM Transactions on Reconfigurable Technology and Systems · 2016
A K-means clustering algorithm involves partitioning of data iteratively into k clusters. It is one of the most popular data-mining algorithms [Wu et al. 2007], and is widely used in other applications, such as image processing and machine learning. However, k-means is highly time-consuming when data or cluster size is large. Traditionally, FPGAs have shown great promise for accelerating computationally intensive algorithms, but they are harder to use for acceleration if we rely on traditional HD-based design methods. The recent introduction of Altera SDK for the OpenCL high-level synthesis tool allows developers to utilize FPGA's potential without long development periods and extensive hardware knowledge. This article presents an optimized implementation of a k-means clustering algorithm on an FPGA using Altera SDK for OpenCL. Performance and power consumption is measured with various data, cluster, and dimension sizes. When compared to state-of-the-art solutions, this implementation supports larger cluster sizes, offers up to 21x speed over a CPU and is more power efficient than a GPU. Unlike previous implementations, it can deliver consistently high throughput across large or small feature dimensions given reasonable cluster sizes and large enough data size.