Massively parallel processing implementation of the toroidal neural networks

The toroidal neural networks (TNN), recently introduced, are derived from discrete time cellular neural network (DT-CNN) and are characterized by an appealing mathematical description which allows the development of an exact learning algorithm. In this work, after reviewing the underlying theory, we describe the implementation of TNN on the APE100/Quadrics massively parallel system and, through an efficiency figure, we show that such type of synchronous SIMD systems are very well suited to support the TNN (and DT-CNN) computational paradigm.