I would try batch normalization, it may help the gradient flow reach the early layers before dying out.
Regarding what a good gradient flow looks like, recall that the gradient influences how much the model is able to learn from an instance of data. Thus, a healthy gradient flow should be non-zero (mostly) from the top layer all the way to the input layer. Otherwise, the weights in the earlier layers will not update at all.
I find it more intuitive to examine the graph from right to left since this is actually how one would compute gradients during backpropagation.