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.