Asynchronous VLSI neural networks using pulse-stream arithmetic

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The relationship between neural networks and VLSI is explored. An introduction to neural networks relates the Hopfield model and the Delta learning rule to S. Grossberg's (1968) description of neural dynamics. A computational style that mimics that of a biological neural network, using pulse-stream signaling and analog summation, is described. Digitally programmable weights allow learning networks to be constructed. Functional and structural forms of neural and synaptic functions are presented, along with simulation results. Finally a neural network implemented in 3- mu m CMOS is presented with preliminary measurements. >