Efficient Implementation of $k$-Nearest Neighbor Classifier Using Vote Count Circuit

The k-nearest neighbor (k-NN) classification is a nonparametric method to classify objects based on the training set. It is an instance-based classifier operating on the assumption that the unknown instance is related to the known ones according to some distance/similarity functions. In this brief, a hardwareassisted algorithm, i.e., vote count, is introduced to approximate the k-NN classifier to provide a low-cost classification solution. It is found that this hardware-assisted solution achieves similar performance as that of the k-NN classifier. In addition, it is highly scalable with respect to the training sample size, which is essential for the k-NN algorithm to deliver its full potential for real-life classification problems.