The irregular data access pattern caused by sparsity brings great challenges to efficient processing accelerators. Focusing on the index-matching property in DNN, this article aims to decompose sparse DNN processing into easy-to-handle processing tasks to maintain the utilization of processing elements. According to the proposed sparse processing dataflow, this article proposes an efficient general-purpose hardware accelerator called MOSDA, which can be effectively applied for operations of convolutional layers, fully-connected layers, and matrix multiplications. Compared to the state-of-art CNN accelerators, MOSDA achieves $1.1 \times $ better throughput and $2.1 \times $ better energy efficiency than Eyeriss v2 in sparse Alexnet in our case study.

中文翻译：

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MOSDA：具有高效索引匹配功能的片上存储器优化的稀疏深度神经网络加速器

稀疏性导致的不规则数据访问模式给高效的处理加速器带来了巨大挑战。针对DNN中的索引匹配属性，本文旨在将稀疏DNN处理分解为易于处理的处理任务，以保持处理元素的利用率。根据提出的稀疏处理数据流，本文提出了一种称为MOSDA的高效通用硬件加速器，该加速器可以有效地应用于卷积层，全连接层和矩阵乘法的运算。与最新的CNN加速器相比，MOSDA可以实现 $ 1.1 \次$ 更好的吞吐量和 $ 2.1 \次$ 在我们的案例研究中，比稀疏Alexnet中的Eyeriss v2具有更好的能源效率。