Mapping Trained Neural Networks to FPNNs
Martin Krčma, Jan Kastil, Zdeněk Kotásek · 2015
This paper introduces a set of methods for mapping the trained neural networks into the lighted grid structured Field Programmable Neural Networks without the use of a training data set. These methods use information obtained from original neural networks such as a network structure, connection weights and biases. The principles of these mapping methods are described and the used grid FPNNs are explained. The results of experiments are presented and summarized.