Neural network using stochastic processing

Abstract

An apparatus and method for implementing a neural network having N nodes coupled to one another by interconnections having interconnect weights T.sub.ij that quantify the influence of node j on node i. The apparatus comprises a node circuit for each node and a data processor. The data processor receives one or more library members, and transmits the interconnect weights to the node circuits. The data processor also stores a current state vector, and receives input data representing a library member to be retrieved. The data processor then performs an iteration in which the current state vector is sent to the node circuits, and an updated state vector is received from the node circuits, the iteration being commenced by setting the current state vector equal to the input data. Each node circuit comprises one or more stochastic processors for multiplying the state vector elements by the corresponding interconnect weights, to determine the updated state vector. Each stochastic processor preferably includes means for generating a pseudorandom sequence of numbers, and using such sequence to encode the interconnect weights and state vector elements into stochastic input signals that are then multiplied by a stochastic multiplier comprising delay means and an AND gate.