High performance silicon intellectual property for K-Nearest Neighbor algorithm

K-Nearest Neighbor (K-NN) is a classification algorithm that is widely applied in pattern recognition and machine learning. Due to real-time requirements of multimedia content analysis in embedded systems for consumer electronics, it is necessary to accelerate K-NN algorithm by hardware implementations. A high performance silicon intellectual property for K-NN is proposed in this paper. The features include the distance calculator supporting both Euclidean distance and Manhattan distance, and a set of ranking processing elements with high computational efficiency. Experiments show that the proposed hardware has the maximum clock frequency 400 MHz with TSMC 90 nm technology.