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Fine-Grained Sensitive Node Hardening for Graph Convolutional Network Systems
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DOI：10.1016/j.sysarc.2026.103737.png)
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Abstract
En 中文
With the rapid development of the commercial space industry, the scale of graph-structured data is expected to grow significantly. Traditional deep neural networks face challenges in processing such data due to limitations in feature extraction and information propagation, driving research into Graph Convolutional Networks. Although advanced AI edge platforms offer high computational efficiency, they remain vulnerable to single-event upsets and face resource constraints when implementing redundancy for high-reliability designs. This paper presents an underlying circuit partitioning strategy and a node sensitivity analysis framework, where circuit nodes, defined as fine-grained sub-units obtained by further partitioning coarse-grained modules, are mapped to physical locations and the resulting mapping is integrated into fault analysis. Unlike coarse-grained hardening methods that overlook node-level sensitivities, the proposed approach allows for precise node-level sensitivity ranking, enabling fine-grained hardening where most needed. Experimental results demonstrate that the proposed strategy achieves fault tolerance comparable to full triple modular redundancy, while delivering improvements in resource hardening efficiency of 1.57 ×, 1.67 ×, and 1.76 ×, and improvements in timing hardening efficiency of 1.36 ×, 1.44 ×, and 1.52 × across the three datasets. Compared to coarse-grained methods, it outperforms in hardening efficiency with only a 1.57 × resource overhead and a minimal 15.9% reduction in worst negative slack.
Keywords:
Graph Convolutional Networks
Circuit Partitioning
Node Sensitivity Analysis
Fault Tolerance
Hardening Efficiency
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