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 multi-purpose 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.