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

Zebra: Memory Bandwidth Reduction for CNN Accelerators With Zero Block Regularization of Activation Maps