  Deploying complex deep learning models on edge devices is challenging because
they have substantial compute and memory resource requirements, whereas edge
devices' resource budget is limited. To solve this problem, extensive pruning
techniques have been proposed for compressing networks. Recent advances based
on the Lottery Ticket Hypothesis (LTH) show that iterative model pruning tends
to produce smaller and more accurate models. However, LTH research focuses on
unstructured pruning, which is hardware-inefficient and difficult to accelerate
on hardware platforms.
  In this paper, we investigate iterative pruning in the context of structured
pruning because structurally pruned models map well on commodity hardware. We
find that directly applying a structured weight-based pruning technique
iteratively, called iterative L1-norm based pruning (ILP), does not produce
accurate pruned models. To solve this problem, we propose two activation-based
pruning methods, Iterative Activation-based Pruning (IAP) and Adaptive
Iterative Activation-based Pruning (AIAP). We observe that, with only 1%
accuracy loss, IAP and AIAP achieve 7.75X and 15.88$X compression on LeNet-5,
and 1.25X and 1.71X compression on ResNet-50, whereas ILP achieves 4.77X and
1.13X, respectively.
