Morph-GCNX: A Universal Architecture for High-Performance and Energy-Efficient Graph Convolutional Network Acceleration

Abstract

While current Graph Convolutional Networks (GCNs) accelerators have achieved notable success in a wide range of application domains, these GCN accelerators can not support various intra- and inter- GCN dataflows or adapt to diverse GCN applications. In this paper, we propose Morph-GCNX, a flexible GCN accelerator architecture for high-performance and energy-efficient GCN execution. The proposed design consists of a flexible Processing Element (PE) array that can be partitioned at runtime and adapt to the computational needs of different layers within a GCN or multiple concurrent GCNs. The proposed Morph-GCNX also consists of a morphable interconnection design to support a wide range of GCN dataflows with various parallelization and data reuse strategies for GCN execution. We also propose a hardware-application co-exploration technique that explores the GCN and hardware design spaces to identify the best PE partition, workload allocation, dataflow, and interconnection configurations, with the goal of improving overall performance and energy. Simulation results show that the proposed Morph-GCNX architecture achieves 18.8×, 2.9×, 1.9×, 1.8×, and 2.5× better performance, reduces DRAM accesses by a factor of 10.8×, 3.7×, 2.2×, 2.5×, and 1.3×, and improves energy consumption by 13.2×, 5.6×, 2.1×, 2.5×, and 1.3×, as compared to prior designs including HyGCN, AWB-GCN, LW-GCN, GCoD, and GCNAX, respectively.