论文标题
斑马：CNN加速器的存储器带宽减小，其激活图的零块正规化
Zebra: Memory Bandwidth Reduction for CNN Accelerators With Zero Block Regularization of Activation Maps
论文作者
论文摘要
本地缓冲区和外部DRAM之间的大量内存带宽已成为CNN硬件加速器的加速瓶颈，尤其是用于激活图。为了减少存储器带宽，我们建议在激活图（Zebra）的零块正则化中，动态地学习不重要的块。该策略的计算开销较低，并且可以轻松地与其他修剪方法集成以提高性能。实验结果表明，所提出的方法可以减少1 \％精度下降中的微型imageNet上的RESNET-18的记忆带宽的70％，而网络缩小的组合结合使用了2 \％的准确度。
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.
