A Silicon Primitive for Competitive Learning

Competitive learning is a technique for training classification and clustering networks. We have designed and fabricated an 11-transistor primitive, that we term an automaximizing bump circuit, that implements competitive learning dynamics. The circuit performs asimilarity computation, affords nonvolatile storage, and implements simultaneous local adaptation and computation. We show that our primitive is suitable for implementing competitive learning in VLSI, and demonstrate its effectiveness in a standard clustering task. 1 Introduction Competitive learning is a family of neural learning algorithms that has proved useful fortraining many classification and clustering networks [1]. In these networks, a neuron's synaptic weight vector typically represents a tight cluster of data points.