  Deep learning is finding its way into the embedded world with applications
such as autonomous driving, smart sensors and aug- mented reality. However, the
computation of deep neural networks is demanding in energy, compute power and
memory. Various approaches have been investigated to reduce the necessary
resources, one of which is to leverage the sparsity occurring in deep neural
networks due to the high levels of redundancy in the network parameters. It has
been shown that sparsity can be promoted specifically and the achieved sparsity
can be very high. But in many cases the methods are evaluated on rather small
topologies. It is not clear if the results transfer onto deeper topologies. In
this paper, the TensorQuant toolbox has been extended to offer a platform to
investigate sparsity, especially in deeper models. Several practical relevant
topologies for varying classification problem sizes are investigated to show
the differences in sparsity for activations, weights and gradients.
