Critical issues in mapping neural networks on message-passing multicomputers

The architectural requirements for efficiently simulating large neural networks on a multicomputer system with thousands of fine-grained processors and distributed memory are investigated. Models for characterizing the structure of a neural network and the function of individual cells are developed. These models provide guidelines for efficiently mapping the network onto multicomputer technologies such as the hypercube, hypernet, and torus. They are further used to estimate the amount of interprocessor communication bandwidth required, and the number of processors needed to meet a particular cost/performance goal. Design issues such as memory organization and the effect of VLSI technology are also considered