Neural network employing leveled summing scheme with blocked array

Abstract

A novel associative network architecture is described in which a neural network is subdivided into a plurality of smaller blocks. Each block comprises an array of pattern matching cells which is used for calculating the relative match, or Hamming distance, between an input pattern and a stored weight pattern. The cells are arranged in columns along one or more local summing lines. The total current flowing along the local summing lines for a given block corresponds to the match for that block. Each of the blocks are coupled together using a plurality of global summing lines. The global summing lines sum the individual current contributions from the local summing lines of each associated block. Coupling between the local column lines and the global summing lines is achieved by using a specialized coupling device which permits control of the coupling ratio between the lines. By selectively turning on or off various blocks a measure of the match for individual blocks or for groups of blocks representing a subset of the network, may be calculated. Control over the coupling ratio within the blocks also prevents destructive levels of current from building up on the global summing lines.