A new learning algorithm for neural networks with integer weights and quantized non-linear activation functions

Abstract

The hardware implementation of neural networks is a fascinating area of research with for reaching applications. However, the real weights and non-linear activation function are not suited for hardware implementation. A new learning algorithm, which trains neural networks with integer weights and excludes derivatives from the training process, is presented in this paper. The performance of this procedure was evaluated by comparing to multi-threshold method and continuous discrete learning method on XOR and function approximation problems, and the simulation results show the new learning method outperforms the other two greatly in convergence and generalization. © 2008 International Federation for Information Processing.