FAST NEURAL NETWORKS WITHOUT MULTIPLIERS

The paper introduces multilayer perceptrons with weight values restricted to powers-of-two or sum of power-of-two. In a digital implementation, these neural networks do not need multipliers but only shift registers when computing in forward mode, thus saving chip area and computation time. A learning procedure, based on back-propagation, is presented for such neural networks. This learning procedure requires full real arithmetic and therefore must be performed off-line. Some test cases are presented, concerning MLP's with hidden layers of different size, on pattern recognition problems. Such tests demonstrate the validity and the generalization capability of the method and give some insight into the behavior of the learning algorithm.