Row-wise Inter-Phase Pipelining for Hardware-Efficient GCN Acceleration

Graph Convolutional Networks (GCNs) are widely deployed for learning on graph-structured data. Yet, their canonical two-phase execution, consisting of feature transformation followed by neighborhood aggregation, suffers from severe inefficiencies on existing accelerators. The prevailing phase-decoupled model explicitly stores dense intermediate results to off-chip memory after transformation and reloads them for aggregation, incurring substantial inter-phase data movement. Additionally, irregular access patterns cause intra-phase redundancy through repeated fetches of sparse inputs. To overcome both forms of redundancy, we propose IPRS-GCN, a streaming accelerator built on Inter-Phase Row-Streaming (IPRS). IPRS fuses transformation and aggregation at row granularity into a single pipeline, forwarding each node’s transformed features directly to its neighbors via the Row Queue as soon as they are computed. This eliminates off-chip storage of intermediates and enforces single-pass streaming access to inputs while keeping weights resident on-chip. The architecture realizes this dataflow through lightweight mechanisms including conflict-aware non-zero packing and degree-adaptive buffering, achieving near-ideal compute utilization with minimal on-chip footprint. Evaluated across five real-world datasets, IPRS-GCN achieves a geometric mean speedup of 107.74 × over an NVIDIA V100 GPU, outperforms HyGCN and AWB-GCN by 7.74 × and 2.12 ×, respectively, and improves energy efficiency by 1.37 ×.