An Analog Programmable Multidimensional Radial Basis Function Based Classifier

A compact analog programmable multidimensional radial basis function (RBF)-based classifier is demonstrated. The probability distribution of each feature in the templates is modeled by a Gaussian function that is approximately realized by the bell-shaped transfer characteristics of a proposed floating-gate circuit, which we term a floating-gate bump circuit. The maximum likelihood, the mean, and the variance of the distribution are stored in floating-gate transistors and are independently programmable. By cascading these floating-gate bump circuits, the overall transfer characteristics approximate a multivariate Gaussian function with a diagonal covariance matrix. An array of these circuits constitute a compact multidimensional RBF-based classifier that can easily implement a Gaussian mixture model. When followed by a winner-take-all circuit, the RBF-based classifier forms an analog vector quantizer. We use receiver operating characteristic curves and equal error rate to evaluate the performance of our RBF-based classifier as well as a resultant analog vector quantizer. We show that the classifier performance is comparable to that of digital counterparts. The proposed approach can be at least two orders of magnitude more power efficient than the digital microprocessors at the same task.