An efficient digital VLSI implementation of Gaussian mixture models-based classifier

Gaussian mixture models (GMM)-based classifiers have shown increased attention in many pattern recognition applications. Improved performances have been demonstrated in many applications, but using such classifiers can require large storage and complex processing units due to exponential calculations and a large number of coefficients involved. This poses a serious problem for portable real-time pattern recognition applications. In this paper, first the performance of GMM and its hardware complexity are analyzed and compared with a number of benchmark algorithms. Next, an efficient digital hardware implementation is proposed. A number of design strategies are proposed in order to achieve the best possible tradeoffs between circuit complexity and real-time processing. First, a serial-parallel vector-matrix multiplier combined with an efficient pipelining technique is used. A novel exponential calculation circuit based on a linear piecewise approximation is proposed to reduce hardware complexity. The precision requirement of the GMM parameters in our classifier are also studied for various classification problems. The proposed hardware implementation features programmability and flexibility offering the possibility to use the proposed architecture for different applications with different topologies and precision requirements. To validate the proposed approach, a prototype was implemented in 0.25-μm CMOS technology and its operation was successfully tested for gas identification application.