Joint Optimization of Cache Management and Graph Reordering for GCN Acceleration

Graph Convolutional Networks (GCNs) have demonstrated their efficacy in various real-world applications such as social networks and recommendation systems. Accelerating GCNs presents unique challenges due to their large number of nodes, sparse and heavily skewed connections. The reordering of the adjacency matrix has been the main strategy to effectively reduce the amount of re-access. Existing techniques of the reordering are categorized into i) degree-based sorting to identify high-degree nodes so that their data could be stored in the cache and ii) graph partitioning to maximally reuse the clustered data. However, as connections among the nodes vary significantly, processing various GCNs with a single strategy would cause performance degradation. This paper presents a software/hardware co-optimized platform for processing of general GCNs. We propose a hybrid scheme in the graph reordering that combines a sorting and a clustering in an adaptively optimized two-way partitioning. The two-way partitioning enables an efficient allocation of the on-chip cache memory space to reduce off-chip memory access by 4-to-12 %. The implemented accelerator in 28nm demonstrates full functionalities with improved energy-efficiency by
$2.2-\text{to}-3.7\times$
compared to the previous GCN accelerators.