Cluster-based Prototype Learning System for Multiple Applications with Flexible HW/SW Codesign

This paper proposes a novel hybrid hardware-software (HW/SW) system for K-means-based prototype learning and Nearest-Neighbor (1-NN) classification. We implement a prototype learning system instead of simplifying complex learning algorithms (e. g. neural and fuzzy networks, or SVMs) because this facilitates the adaptability to hardware capabilities and constraints. The K-means algorithm, which is implemented by HW/SW co-design, is effective in improving classification performance and reducing storage requirements. Particularly, the hardware realization is applied to obtain orders of magnitude higher speed for nearest-distance searching, which is the most burdensome performance barrier both in K-means learning and 1-NN classification. We benchmark our multipurpose learning system against the application of handwritten digit recognition and face recognition to demonstrate its excellent performance, namely high flexibility, fast training, short recognition time and good recognition rate.