Parallel computing algorithm of neural networks on an eight-neighbor processor array

The authors describe a parallel computing algorithm to simulate the backpropagation (BP) model and Kohonen´s self-organizing feature map (SOFM) upon an eight-neighbor processor array. Taking account of the parallelism intrinsically found in neural networks, algorithms are presented which minimize the transmission overhead among processors, so that high-speed simulation of neural networks becomes feasible. The processing time required for one learning of BP or Kohonen´s SOFM for one input vector is estimated