Efficient implementation of Gaussian Mixture Models using vote count circuit

Vote count (VC) is a fast search algorithm originally designed for similarity search on large scale data set. VC can be efficiently implemented using simple modification to the Random Access Memory (RAM) or other memory structures such as NOR or NAND Flash memory, such that the search complexity reduces to O(1) regardless of the dimensionality of data or the size of the data set. This paper proposes a low complexity implementation for the posterior probability calculation of Gaussian Mixture Models (GMM) using the VC circuit. The performance of the proposed implementation is evaluated in terms of both accuracy of the posterior probability calculation, and classification error rate if GMM is used as a classifier.