Abstract: A stochastic data processing technique in which a conversion signal is generated representing a pseudorandom sequence of numbers. The conversion signal is then used to convert input signals representing a pair of input operands into respective first and second stochastic signals. The same conversion signal is used to encode both operands. A primitive data processing operation is then performed on the first and second stochastic signals to produce an output signal that may be converted back into nonstochastic form. The primitive data processing operations include MAX, MIN, absolute difference, arithmetic mean and multiplication. For multiplication, one of the stochastic signals is subjected to a predetermined delay.

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.