A 384G Output NonZeros/J Graph Convolutional Neural Network Accelerator

This brief presents the first IC implementation of graph convolutional neural network (GCN) accelerator chip. A sparsity aware dataflow optimized for sub-block-wise processing of three different matrices in GCN is proposed to improve the utilization ratio of computing resources while reducing the amount of redundant access of off-chip memory. The implemented accelerator in 28-nm CMOS produces 384G NZ outputs/J for the extremely sparse matrix multiplications of the GCN. It shows 58k-to-143k, 38k-to-92k and 5k-to-13k Graph/J for the benchmark graph datasets of Cora, Citeseer and Pubmed, respectively. The energy efficiency in Graph/J of the proposed 16b ASIC implementation shows about 4-to- $11\mathbf {\times }$ and 8-to- $25\mathbf {\times }$ improvements compared to the previously reported 8b FPGA and 32b FPGA implementations, respectively.