GCN-ABFT: Low-Cost Online Error Checking for Graph Convolutional Networks
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917878886825984 |
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| author | Peltekis, Christodoulos Dimitrakopoulos, Giorgos |
| author_facet | Peltekis, Christodoulos Dimitrakopoulos, Giorgos |
| contents | Graph convolutional networks (GCNs) are popular for building machine-learning application for graph-structured data. This widespread adoption led to the development of specialized GCN hardware accelerators. In this work, we address a key architectural challenge for GCN accelerators: how to detect errors in GCN computations arising from random hardware faults with the least computation cost. Each GCN layer performs a graph convolution, mathematically equivalent to multiplying three matrices, computed through two separate matrix multiplications. Existing Algorithm-based Fault Tolerance(ABFT) techniques can check the results of individual matrix multiplications. However, for a GCN layer, this check should be performed twice. To avoid this overhead, this work introduces GCN-ABFT that directly calculates a checksum for the entire three-matrix product within a single GCN layer, providing a cost-effective approach for error detection in GCN accelerators. Experimental results demonstrate that GCN-ABFT reduces the number of operations needed for checksum computation by over 21% on average for representative GCN applications. These savings are achieved without sacrificing fault-detection accuracy, as evidenced by the presented fault-injection analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18534 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GCN-ABFT: Low-Cost Online Error Checking for Graph Convolutional Networks Peltekis, Christodoulos Dimitrakopoulos, Giorgos Hardware Architecture Machine Learning Graph convolutional networks (GCNs) are popular for building machine-learning application for graph-structured data. This widespread adoption led to the development of specialized GCN hardware accelerators. In this work, we address a key architectural challenge for GCN accelerators: how to detect errors in GCN computations arising from random hardware faults with the least computation cost. Each GCN layer performs a graph convolution, mathematically equivalent to multiplying three matrices, computed through two separate matrix multiplications. Existing Algorithm-based Fault Tolerance(ABFT) techniques can check the results of individual matrix multiplications. However, for a GCN layer, this check should be performed twice. To avoid this overhead, this work introduces GCN-ABFT that directly calculates a checksum for the entire three-matrix product within a single GCN layer, providing a cost-effective approach for error detection in GCN accelerators. Experimental results demonstrate that GCN-ABFT reduces the number of operations needed for checksum computation by over 21% on average for representative GCN applications. These savings are achieved without sacrificing fault-detection accuracy, as evidenced by the presented fault-injection analysis. |
| title | GCN-ABFT: Low-Cost Online Error Checking for Graph Convolutional Networks |
| topic | Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2412.18534 |