DECOMPOSITION AND MAPPING OF LOCALLY CONNECTED LAYERED NEURAL NETWORKS ON MESSAGE-PASSING MULTIPROCESSORS

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Abstract
In this paper we present an integrated model for decomposition and mapping (D&M) of Locally Connected Layered Neural Networks (LCLNs) on message-passing multiprocessors. Within the framework of this model we analyze two previously proposed D&M strategies for a particular class of LCLNs. The model is compared with the performance of a neural network simulation environment, running on a transputer array. We find that both strategies may be applicable, depending on the network size, and we can determine the problem size at which one strategy is preferred over the other. Furthermore, we find that the regularity of the communication pattern between the processors is an unexpected factor in the D&M decision.