SAGA: Sparsity-Agnostic Graph Convolutional Network Acceleration with Near-optimal Workload Balance

Graph Convolutional Networks (GCNs) have shown much promise in resolving sophisticated scientific problems with non-Euclidean data, such as traffic prediction, disease classification, and many others. However, the irregular sparsity of real-world graphs remains a major challenge toward efficient GCN acceleration. In this paper, we propose SAGA, a Sparsity-Agnostic Graph Convolutional Accelerator with near-optimal workload balance. Specifically, it consists of two unique features, an NZ-based scheduling, and a novel accelerator architecture. Unlike conventional GCN accelerators with uneven distribution of sparse matrix, the proposed NZ-based scheduling leverages the metadata encoded in the compression format to enable even distribution of sparse matrix at runtime, thus achieving near-optimal workload balancing. In addition, the proposed architecture, including a task scheduler, an accumulation table, and a partial row accumulation unit, can support the proposed NZ-based scheduling without data preprocessing and reformatting with low overheads. We prototyped the proposed design through FPGAs, and our evaluation results show that SAGA achieves up to 1.56x speedup and 2.05x energy savings on average as compared to the prior art [1].