Mapping of artificial neural networks onto message passing systems

Various Artificial Neural Networks (ANNs) have been proposed in recent years to mimic the human brain in solving problems involving human-like intelligence. Efficient mapping of ANNs comprising of large number of neurons onto various distributed MIMD architectures is discussed in this paper. The massive interconnection among neurons demands a communication efficient architecture. Issues related to the suitability of MIMD architectures for simulating neural networks are discussed. Performance analysis of ring, torus, binary tree, hypercube, and extended hypercube for simulating artificial neural networks is presented. Our studies reveal that the performance of the extended hypercube is better than those of ring, torus, binary tree, and hypercube topologies