  We investigate pruning and quantization for deep neural networks. Our goal is
to achieve extremely high sparsity for quantized networks to enable
implementation on low cost and low power accelerator hardware. In a practical
scenario, there are particularly many applications for dense prediction tasks,
hence we choose stereo depth estimation as target.
  We propose a two stage pruning and quantization pipeline and introduce a
Taylor Score alongside a new fine-tuning mode to achieve extreme sparsity
without sacrificing performance.
  Our evaluation does not only show that pruning and quantization should be
investigated jointly, but also shows that almost 99% of memory demand can be
cut while hardware costs can be reduced up to 99.9%. In addition, to compare
with other works, we demonstrate that our pruning stage alone beats the
state-of-the-art when applied to ResNet on CIFAR10 and ImageNet.
