Graph convolutional networks (GCNs) have been applied successfully in social networks and recommendation systems to analyze graph data. Unlike conventional neural networks, GCNs introduce an aggregation phase, which is both computation- and memory-intensive. This phase aggregates features from the neighboring vertices in the graph, which incurs significant amounts of irregular data and memory access. The emerging computation-in-memory (CIM) architecture presents a promising solution to alleviate the problem of irregular accesses and provide fast near-data processing for GCN applications by integrating both three-dimensional stacked CIM and general-purpose processing units in the system. This paper presents Graph-CIM, which exploits the hybrid CIM architecture to determine the allocation of GCN applications. Graph-CIM models the GCN application process as a directed acyclic graph (DAG) and allocates tasks on the hybrid CIM architecture. It achieves fine-grained graph partitioning to capture the irregular characteristics of the aggregation phase of GCN applications. We use a set of representative GCN models and standard graph datasets to evaluate the effectiveness of Graph-CIM. The experimental results show that Graph-CIM can significantly reduce the processing latency and data-movement overhead compared with the representative schemes.

图卷积网络（GCN）已成功应用于社交网络和推荐系统中以分析图数据。与传统的神经网络不同，GCN引入了聚合阶段，该阶段既需要大量计算，也需要占用大量内存。此阶段聚合了图形中相邻顶点的特征，这会导致大量不规则数据和内存访问。新兴的内存中计算（CIM）架构提出了一种有前途的解决方案，可通过在系统中集成三维堆叠CIM和通用处理单元来缓解不规则访问问题，并为GCN应用提供快速的近数据处理。本文介绍了Graph-CIM，它利用混合CIM体系结构来确定GCN应用程序的分配。Graph-CIM将GCN应用过程建模为有向非循环图（DAG），并在混合CIM体系结构上分配任务。它实现了细粒度的图分区，以捕获GCN应用程序聚合阶段的不规则特性。我们使用一组具有代表性的GCN模型和标准图形数据集来评估Graph-CIM的有效性。实验结果表明，与典型方案相比，Graph-CIM可以显着减少处理延迟和数据移动开销。我们使用一组具有代表性的GCN模型和标准图形数据集来评估Graph-CIM的有效性。实验结果表明，与典型方案相比，Graph-CIM可以显着减少处理延迟和数据移动开销。我们使用一组具有代表性的GCN模型和标准图形数据集来评估Graph-CIM的有效性。实验结果表明，与典型方案相比，Graph-CIM可以显着减少处理延迟和数据移动开销。