K-means clustering algorithm for multimedia applications with flexible HW/SW co-design

In this paper, we report a hardware/software (HW/SW) co-designed K-means clustering algorithm with high flexibility and high performance for machine learning, pattern recognition and multimedia applications. The contributions of this work can be attributed to two aspects. The first is the hardware architecture for nearest neighbor searching, which is used to overcome the main computational cost of a K-means clustering algorithm. The second aspect is the high flexibility for different applications which comes from not only the software but also the hardware. High flexibility with respect to the number of training data samples, the dimensionality of each sample vector, the number of clusters, and the target application, is one of the major shortcomings of dedicated hardware implementations for the K-means algorithm. In particular, the HW/SW K-means algorithm is extendable to embedded systems and mobile devices. We benchmark our multi-purpose K-means system against the application of handwritten digit recognition, face recognition and image segmentation to demonstrate its excellent performance, high flexibility, fast clustering speed, short recognition time, good recognition rate and versatile functionality.