3D-SubG: A 3D Stacked Hybrid Processing Near/In-Memory Accelerator for Subgraph GNNs

Subgraph Graph Neural Networks (GNNs) are emerging as a promising approach to enhance GNN expressiveness, but their more complex graph structures with numerous independent and irregular subgraphs pose significant hardware deployment challenges. In this work, we propose 3D-SubG, a 3D stacked hybrid processing-near/in-memory accelerator for subgraph GNNs. With hybrid bonding packaging technology, a logic die is 3D stacked with a DRAM die for highly parallel memory accesses. The logic die employs digital SRAM-based processing-in-memory (PIM) macros to boost computation density and minimize data transfer. We further propose a bit-level non-zero gathering method to exploit graph sparsity for PIM, a workloadbalanced mapping strategy for subgraph allocation onto different logic-to-DRAM blocks, and a distributed global pooling approach to reduce inter-block data movements. Experimental results show that 3D-SubG achieves average improvements of $146.11 \times$ in performance, $934.18 \times$ in area efficiency, and $1171.80 \times$ in energy efficiency compared to RTX 3090Ti.