Convolutional neural networks (CNNs) have achieved great success in performing cognitive tasks. However, execution of CNNs requires a large amount of computing resources and generates heavy memory traffic, which imposes a severe challenge on computing system design. Through optimizing parallel executions and data reuse in convolution, systolic architecture demonstrates great advantages in accelerating CNN computations. However, regular internal data transmission path in traditional systolic architecture prevents the systolic architecture from completely leveraging the benefits introduced by neural network sparsity. Deployment of fine-grained sparsity on the existing systolic architectures is greatly hindered by the incurred computational overheads. In this work, we propose S2Engine $-$ a novel systolic architecture that can fully exploit the sparsity in CNNs with maximized data reuse. S2Engine transmits compressed data internally and allows each processing element to dynamically select an aligned data from the compressed dataflow in convolution. Compared to the naive systolic array, S2Engine achieves about $3.2\times$ and about $3.0\times$ improvements on speed and energy efficiency, respectively.

中文翻译：

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S2Engine：一种用于稀疏卷积神经网络的新型脉动架构

卷积神经网络 (CNN) 在执行认知任务方面取得了巨大成功。然而，CNNs的执行需要大量的计算资源并产生大量的内存流量，这对计算系统设计提出了严峻的挑战。通过优化卷积中的并行执行和数据重用，脉动架构在加速 CNN 计算方面表现出巨大优势。然而，传统收缩架构中的常规内部数据传输路径阻止了收缩架构完全利用神经网络稀疏性带来的好处。在现有的收缩架构上部署细粒度稀疏性受到计算开销的极大阻碍。在这项工作中，我们提出了 S2Engine $-$ 一种新颖的脉动架构，可以充分利用 CNN 中的稀疏性并最大限度地重用数据。S2Engine 在内部传输压缩数据，并允许每个处理元素在卷积中从压缩数据流中动态选择对齐的数据。与简单的脉动阵列相比，S2Engine 在速度和能源效率方面分别实现了约 3.2 美元和约 3.0 美元的改进。