  Deep neural networks (DNNs) are shown to be promising solutions in many
challenging artificial intelligence tasks. However, it is very hard to figure
out whether the low precision of a DNN model is an inevitable result, or caused
by defects. This paper aims at addressing this challenging problem. We find
that the internal data flow footprints of a DNN model can provide insights to
locate the root cause effectively. We develop DeepMorph (DNN Tomography) to
analyze the root cause, which can guide a DNN developer to improve the model.
