为图神经网络加速器设计低成本在线错误检测方法
GCN-ABFT: Low-Cost Online Error Checking for Graph Convolutional Networks
- 直接对三矩阵乘积计算校验和，避免重复检查
- 平均减少21%以上校验计算量，不降低检测精度
- 适合部署在资源受限的GCN硬件加速器中
图卷积网络（GCNs）广泛用于处理图结构数据，推动了专用GCN硬件加速器的发展。本文针对GCN加速器中的关键挑战——如何以最低计算开销检测随机硬件故障引起的计算错误，提出GCN-ABFT方法。每个GCN层的图卷积可数学等价为三个矩阵相乘，通过两次矩阵乘法实现。现有基于算法的容错（ABFT）技术仅能检查单次矩阵乘法结果，需对每层执行两次校验。为此，本工作提出直接在单个GCN层内计算三矩阵乘积的整体校验和，实现低开销错误检测。实验表明，对于代表性GCN应用，该方法平均减少超过21%的校验计算操作量，且经故障注入分析验证，未牺牲故障检测准确性。
原文摘要 · Abstract (English)
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.
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