Boosting neural network feature extraction by reduced accuracy activation functions

摘要

The paper proposes the use of a lookup table in the computation of the sigmoid function in multilayer perceptron feature extraction. The approach is frequently used in limited precision hardware implementations. This paper considers its use as a software implementation. It is argued that the method is computationally efficient and does not compromise the performance of the neural networks. This new approach is analysed and evaluated in both theoretical and experimental terms. The practical study is performed using several real world and synthetic data sets. The results show that an increase in computational efficiency is achieved with very little or no influence on the quality of the extracted features.