An advanced software model for optimization of self-organizing neural networks oriented on implementation in hardware

In this paper we present an advanced software tool designed for a multi-criteria optimization of self-organizing neural networks (SOMs) for their effective implementation in hardware. Problems that we have to deal with in this type of implementations are radically different from those that occur in only pure software realizations. Therefore, although there are many available systems to simulate NNs, they are not useful for our purposes. The proposed system allows to investigate the influence of various physical constraints on the learning process of the NN. It enables a modification of more than sixty parameters, so almost any learning scenario, as well as almost each configuration of the NN can be tested. It is possible to run multiple tests in accordance with a created lists of tasks, in which particular parameters are changed in loops with a certain range and with a given step. This allows to carry out in a relatively short time thousands of simulations for different combinations of particular parameters. Finally, it allows to select the most efficient combinations of the parameters looking from the point of view of the effective transistor level implementation.