Learning Structured Sparsity in Deep Neural Networks
1. 文章介绍 DNN，尤其是CNN，已经通过从大量数据中的大规模学习在计算机视觉领域取得了巨大的成功，但是这样的大模型部署是有问题的。为了减少计算成本，许多研究方法被用来压缩DNN的大小，包括稀疏正则化、连接剪枝和低秩近似，稀疏正则化和连接剪枝通常会得到一个非结构化的随机连接关系，因此不规则的内存访问不利于硬件加速，Figure 1展示了通过L1正则化对AlexNet每一层的非结构稀疏化。与原始
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相关文章
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