A Unified Engine for Accelerating GNN Weighting/Aggregation Operations, With Efficient Load Balancing and Graph-Specific Caching
Sudipta Mondal, Susmita Dey Manasi, Kishor Kunal, S Ramprasath, Ziqing Zeng, Sachin S. Sapatnekar · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · 2022
Graph neural networks (GNNs) analysis engines are vital for real-world problems that use large graph models. Challenges for a GNN hardware platform include the ability to 1) host a variety of GNNs; 2) handle high sparsity in input vertex feature vectors and the graph adjacency matrix and the accompanying random memory access patterns; and 3) maintain load-balanced computation in the face of uneven workloads, induced by high sparsity and power-law vertex degree distributions. This article proposes GNNIE, an accelerator designed to run a broad range of GNNs. It tackles workload imbalance by 1) splitting vertex feature operands into blocks; 2) reordering and redistributing computations; and 3) using a novel flexible MAC architecture. It adopts a graph-specific, degree-aware caching policy that is well suited to real-world graph characteristics. The policy enhances on-chip data reuse and avoids random memory access to DRAM. GNNIE achieves average speedups of$7197\times $over a CPU and$17.81\times $over a GPU over multiple datasets on graph attention networks (GATs), graph convolutional networks (GCNs), GraphSAGE, GINConv, and DiffPool. Compared to prior approaches, GNNIE achieves an average speedup of$5\times $over HyGCN (which cannot implement GATs) for GCN, GraphSAGE, and GINConv. GNNIE achieves an average speedup of$1.3\times $over AWB-GCN (which runs only GCNs), despite using$3.4\times $fewer processing units.