The idea in behind is that neural networks are (in general) overparametrized. In theory, the more parameters it a DNN has, the easier it converges.
Idea in behind is that once a DNN has been trained you can get rid off these non useful parameters. I don’t know how it is implemented in practice. I guess it will be some kind of sparse tensor.