ON THE SYSTOLIC IMPLEMENTATION OF ASSOCIATIVE MEMORY ARTIFICIAL NEURAL NETWORKS

This paper describes the systolic implementation of a class of Artificial Neural Networks generally termed as Associative Memories. Such networks are the Discrete Autocorrelator or Discrete Hopfield, the Bi-directional Associative Memory, the Temporal Associative Memory, the Linear Associative Memory and the Novelty Filter. Further, problems such as different weight updating strategies, higher order correlation techniques and Fuzzy pattern encoding are discussed. The Re-Usable Matrix Vector and Matrix Multiplication Systolic Arrays form the basis of the systolic implementation.