  The large amount of memory bandwidth between local buffer and external DRAM
has become the speedup bottleneck of CNN hardware accelerators, especially for
activation maps. To reduce memory bandwidth, we propose to learn pruning
unimportant blocks dynamically with zero block regularization of activation
maps (Zebra). This strategy has low computational overhead and could easily
integrate with other pruning methods for better performance. The experimental
results show that the proposed method can reduce 70\% of memory bandwidth for
Resnet-18 on Tiny-Imagenet within 1\% accuracy drops and 2\% accuracy gain with
the combination of Network Slimming.
