A Full-Stack Framework for GNN Acceleration via Partition-Compiler-Architecture Co-Design

Graph Neural Networks (GNNs) have achieved remarkable success across domains such as recommendation and scientific computing, yet their practical deployment remains constrained by high execution cost. The diversity of GNN model structures and the sparsity of real-world graphs pose two fundamental challenges for hardware acceleration: supporting heterogeneous operator patterns and achieving high resource utilization under irregular data access. Existing accelerators often address only one aspect, either targeting specific models with hardwired pipelines or applying general architectures with limited efficiency. To address these challenges, we propose SWITCHBLADE, a full-stack framework for GNN acceleration through the coordinated design of partitioning, compilation, and architecture. SWITCHBLADE addresses these challenges through three key components. First, a phase-based intermediate representation unifies diverse GNN models by abstracting computation stages for model-independent code generation. Second, a fine-grained graph partitioner enhances data locality and reduces memory traffic by adapting to graph topology and model semantics. Third, the hardware architecture supports stream-level parallelism and decoupled execution to exploit cross-shard and inter-phase concurrency. Evaluation on representative models and datasets shows that SWITCHBLADE achieves up to 1.85× speedup and 19.03× energy savings over an NVIDIA V100 GPU, while outperforming state-of-the-art GNN accelerators across diverse full-graph workloads, demonstrating both high efficiency and broad model generality.