@inproceedings{57b9a3d464bd4d5da7e60700b3f06ca3,
title = "HFGCN: High-speed and Fully-optimized GCN Accelerator",
abstract = "graph convolutional network (GCN) is a type of neural network that inference new nodes based on the connectivity of the graphs. GCN requires high-calculation volume for processing, similar to other neural networks requiring significant calculation. In this paper, we propose a new hardware architecture for GCN that tackles the problem of wasted cycles during processing. We propose a new scheduler module that reduces memory access through aggregation and an optimized systolic array with improved delay. We compare our study with the state-of-the-art GCN accelerator and show outperforming results.",
author = "Han, {Min Seok} and Jiwan Kim and Donggeon Kim and Hyunuk Jeong and Gilho Jung and Myeongwon Oh and Hyundong Lee and Yunjeong Go and Kim, {Hyun Woo} and Jongbeom Kim and Taigon Song",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 24th International Symposium on Quality Electronic Design, ISQED 2023 ; Conference date: 05-04-2023 Through 07-04-2023",
year = "2023",
doi = "10.1109/ISQED57927.2023.10129340",
language = "English",
series = "Proceedings - International Symposium on Quality Electronic Design, ISQED",
publisher = "IEEE Computer Society",
booktitle = "Proceedings of the 24th International Symposium on Quality Electronic Design, ISQED 2023",
address = "United States",
}