ChainPIM: A ReRAM-Based Processing-in-Memory Accelerator for HGNNs via Chain Structure

Abstract

Heterogeneous graph neural networks (HGNNs) have recently demonstrated significant advantages of capturing powerful structural and semantic information in heterogeneous graphs. Different from homogeneous graph neural networks directly aggregating information based on neighbors, HGNNs aggregate information based on complex metapaths. ReRAM-based processing-in-memory (PIM) architecture can reduce data movement and compute matrix-vector multiplication (MVM) in analog. It can be well used to accelerate HGNNs. However, the complex metapath-based aggregation of HGNNs makes it challenging to efficiently utilize the parallelism of ReRAM and vertices data reuse. To this end, we propose ChainPIM, the first ReRAM-based processing-in-memory accelerator for HGNNs featuring high-computing parallelism and vertices data reuse. Specifically, we introduce R-chain, which is based on a chain structure to build related metapath instances together. We can efficiently reuse vertices through R-chain and process different R-chains in parallel. Then, we further design an efficient storage format for storing R-chains, which reduces a lot of repeated vertices storage. Finally, a specialized ReRAM-based architecture is developed to pipeline different types of aggregations in HGNNs, fully exploiting the huge potential of multilevel parallelism in HGNNs. Our experiments show that ChainPIM achieves an average memory space reduction of 47.86% and performance improvement by $128.29\times $ compared to NVIDIA Tesla V100 GPU.