A Review on Important Issues in GCN Accelerator Design

DOI

10.2991/assehr.k.220110.217How to use a DOI?

Keywords

Graph convolutional neural network; hardware acceleration; accelerator architecture; sparse matrix multiplication

Abstract

Graph convolutional neural networks (GCNs) emerge as an efficient method to process real-world graph data and have been proved powerful in various areas like link prediction and crack detection. While the graphs in GCNs have dynamic and irregular inherent patterns, traditional hardware architectures have poor performance on GCN models and accelerators are needed. This review discusses four bottlenecks for traditional hardware which are also important issues in GCN accelerator designs. The irregular patterns of the input matrix harm the utilization of processing elements (PEs), especially for systolic array-based architectures, which can be alleviated by adopting flexible execution patterns. Then there is a problem of high adjacent matrix sparsity which decreases performance. Usual solutions include using flexible loading patterns and preprocessing adjacent matrix to reduce sparsity. The imbalanced workload in the aggregation stage makes PE utilization drop to as low as 18.3%, increasing processing latency. Therefore, a specially designed hardware architecture that enables the exchange of workloads may be efficient. Last, this review discusses the data reuse problem, which is crucial for saving memory resources. Inner product, outer product and some useful techniques are mentioned.

Copyright

© 2022 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article under the CC BY-NC license.

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TY  - CONF
AU  - Siyuan Miao
PY  - 2022
DA  - 2022/01/28
TI  - A Review on Important Issues in GCN Accelerator Design
BT  - Proceedings of the 2021 International Conference on Public Art and Human Development ( ICPAHD 2021)
PB  - Atlantis Press
SP  - 1158
EP  - 1162
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.220110.217
DO  - 10.2991/assehr.k.220110.217
ID  - Miao2022
ER  -