An analog programmable multi-dimensional radial basis function based classifier

A compact analog programmable multi-dimensional radial basis function (RBF) based classifier is demonstrated. The probability distribution of each feature in the templates modeled by a Gaussian function is approximately realized by the transfer characteristics of a floating-gate bump circuit. The maximum likelihood, the mean, and the variance can be inde- pendently programmed. By cascading these floating-gate bump circuits, the transfer characteristics approximate a multivariate Gaussian function with a diagonal covariance matrix. An array of these circuits constitute a compact multi-dimensional RBF- based classifier. 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 analog classifiers. We show that the analog 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.