新的校验和方法增强了边缘设备上CNN的故障检测能力

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一种名为Carry-Through Checksum的新型轻量级故障检测技术，用于边缘应用中的卷积神经网络（CNN）。该方法将校验和嵌入到卷积层中，在整个推理过程中计算和传播校验和，从而以最小的开销实现端到端的错误检测。实验表明，该技术在FP32中可检测到超过95%的关键故障，在FP16中可检测到超过86%的关键故障，而用于缓解的重新执行仅增加了嵌入式GPU上很小的运行时开销。 AI

影响提高了资源受限的边缘环境中AI模型的可靠性，这对于安全关键型应用至关重要。

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Kyrylo Nazarevych, Mohammad Hasan Ahmadilivani, Krister Kaldre, Davide Bertozzi, Jaan Raik · 2026-09-16 04:00

Carry-Through Checksum: A Lightweight Fault-Detection for CNN Inference at the Edge

arXiv:2609.16742v1 Announce Type: cross Abstract: Convolutional Neural Networks (CNNs) are increasingly deployed in safety-critical edge applications, where soft errors can silently corrupt inference outputs and lead to unsafe decisions. Such applications typically rely on resour…

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Carry-Through Checksum: A Lightweight Fault-Detection for CNN Inference at the Edge

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