A charge-based neural Hamming classifier

Abstract

A charge-based fixed-weight neural Hamming classifier with an on-chip normalization facility is described. The classifier utilizes a purely capacitive synapse matrix for quantization and a multiport sense amplifier for discrimination. The discriminator is compatible with variable-weight synapses as well. A detailed analysis of the classifier configuration is presented; design issues are addressed, and limitations are identified. It is shown that the ratio of the maximum Hamming weight to the minimum Hamming distance that can be handled by the classifier has an upper bound. As long as the exemplars comply with this upper bound, the network does not impose any limitation on the word length. A very large exemplar count, on the other hand, can impair connection density, but this problem can be averted by using multiple discriminators. A 2- mu m p-well CMOS test chip containing a Hamming classifier of ten 20-b-long exemplars is described. >