A 384G Output NonZeros/J Graph Convolutional Neural Network Accelerator
IEEE Transactions on Circuits and Systems II: Express Briefs(2022)
Abstract
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-11x and 8-to-25x improvements compared to the previously reported 8b FPGA and 32b FPGA implementations, respectively.
MoreTranslated text
Key words
Graph convolutional neural network (GCN),hardware accelerator,machine learning accelerator,sparse matrix multiplication,application-specific integrated circuit (ASIC)
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined