Analog VLSI implementation of support vector machine learning and classification

We propose an analog VLSI approach to implementing the projection neural networks adapted for the support vector machine with radial-basis kernel functions, which are realized by a proposed floating-gate bump circuit with the adjustable width. Other proposed circuits include simple current mirrors and log-domain Alters. Neither resistors nor amplifiers are employed. Therefore it is suitable for large-scale neural network implementations. We show the measurement results of the bump circuit and verify the resulting analog signal processing system on the transistor level by using a SPICE simulator. The same approach can also be applied to the support vector regression. With these analog signal processing techniques, a low-power adaptive analog system without any analog-to-digital convertor but with the capability of learning, classifying, and regressing becomes feasible.