A programmable analog radial-basis-function based classifier

Abstract

A 16 x 16 programmable analog radial-basis-function (RBF) based classifier is demonstrated. The distribution of each feature is modeled by a Gaussian function, which is realized by a proposed floating-gate bump circuit having bell-shaped transfer characteristics. The maximum likelihood, mean, and 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 distribution with a diagonal covariance matrix. An array of these circuits constitutes a compact RBF-based classifier. When followed by a winner-take-all circuit, the analog classifier can implement vector quantization. Automatic gender identification is implemented on a 16 x 16 analog vector quantizer chip as one possible audio application of this work. The performance of the analog classifier is comparable to that of digital counter -parts. The proposed approach can be at least two orders of magnitude more power efficient than the digital microprocessors at the same task.