Training fast mixed-signal neural networks for data classification

This work presents an incremental training approach that allows for the use of simple evolutionary algorithms to efficiently train the weights of fast mixed-signal hardware neural networks. The training strategy is tested on a set of common classification benchmark problems (E.coli, yeast, thyroid and wine) and the results are comparable to those of other investigations. They demonstrate that the used hardware neural network architecture can successfully be trained to solve real-world problems. It is also shown that the presented training strategy is suited for use in conjunction with a specialized coprocessor that speeds up evolutionary algorithms by performing the genetic operations within a configurable logic. With the ability to integrate the coprocessor into the training strategy, an important prerequisite is fulfilled to exploit the speed of the neural hardware during training.