SPA-GCN：高效灵活的GCN加速器，应用于图形相似性计算

虽然有很多关于图像深度学习的硬件加速的研究，但对涉及图的深度学习应用加速的关注却相当有限。当算法被映射到CPU或GPU上时，图的独特特性，如不规则的内存访问和动态并行性，带来了一些挑战。为了解决这些挑战，同时利用所有可用的稀疏性，我们提出了一个灵活的架构，称为SPA-GCN，用于加速图形卷积网络（GCN），这是图形上深度学习算法的核心计算单元。该架构专门用于处理许多小图，因为图的大小对设计考虑有很大影响。在这种情况下，我们使用SimGNN，一种基于神经网络的图匹配算法，作为案例研究来证明我们架构的有效性。实验结果表明，与多核CPU实现和GPU实现相比，SPA-GCN可以提供较高的速度，显示了我们设计的效率。

原文题目：SPA-GCN: Efficient and Flexible GCN Accelerator with an Application for Graph Similarity Computation

原文：While there have been many studies on hardware acceleration for deep learning on images, there has been a rather limited focus on accelerating deep learning applications involving graphs. The unique characteristics of graphs, such as the irregular memory access and dynamic parallelism, impose several challenges when the algorithm is mapped to a CPU or GPU. To address these challenges while exploiting all the available sparsity, we propose a flexible architecture called SPA-GCN for accelerating Graph Convolutional Networks (GCN), the core computation unit in deep learning algorithms on graphs. The architecture is specialized for dealing with many small graphs since the graph size has a significant impact on design considerations. In this context, we use SimGNN, a neural-network-based graph matching algorithm, as a case study to demonstrate the effectiveness of our architecture. The experimental results demonstrate that SPA-GCN can deliver a high speedup compared to a multi-core CPU implementation and a GPU implementation, showing the efficiency of our design.