A Silicon Primitive for Competitive Learning

Part of Advances in Neural Information Processing Systems 13 (NIPS 2000)

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David Hsu, Miguel Figueroa, Chris Diorio


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 per(cid:173) forms a similarity 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.