A Programmable Multi-Dimensional Analog Radial-Basis- Function-Based Classifier

  • Sheng-Yu Peng
  • Paul E. Hasler
  • David V. Anderson
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 291)

A compact analog programmable multidimensional radial- basis-function (RBF)-based classifier is demonstrated in this chapter. 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 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 char- acteristics approximate a multivariate Gaussian function with a diagonal covariance matrix. An array of these circuits constitute a compact multi- dimensional 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. Receiver operating characteristic curves and equal error rate are used to evaluate the per- formance of the RBF-based classifier as well as a resultant analog vector quantizer. It is shown 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.


Equal Error Rate nMOS Transistor pMOS Transistor Diagonal Covariance Matrix Bump Width 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag US 2009

Authors and Affiliations

  • Sheng-Yu Peng
    • 1
  • Paul E. Hasler
    • 1
  • David V. Anderson
    • 1
  1. 1.School of Electrical and Computer EngineeringGeorgia Institute of TechnologyGeorgia

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