通过故障感知训练提升神经网络抗单粒子翻转能力
Mitigating multiple single-event upsets during deep neural network inference using fault-aware training
- 在模型层面注入故障，分析多重单比特翻转影响
- 故障感知训练使模型容错能力提升3倍
- 无需硬件改动，适合航天等高辐射场景
深度神经网络（DNN）在安全关键应用中日益普及。在高辐射环境中，可靠的故障分析与缓解对保障其功能至关重要。本研究通过在DNN模型层面进行故障注入，分析多重单比特单粒子翻转的影响。此外，提出一种故障感知训练（FAT）方法，可在不修改硬件的前提下提升DNN对故障的鲁棒性。实验结果表明，FAT方法可使模型对故障的容忍度提升达3倍。
原文摘要 · Abstract (English)
Deep neural networks (DNNs) are increasingly used in safety-critical applications. Reliable fault analysis and mitigation are essential to ensure their functionality in harsh environments that contain high radiation levels. This study analyses the impact of multiple single-bit single-event upsets in DNNs by performing fault injection at the level of a DNN model. Additionally, a fault aware training (FAT) methodology is proposed that improves the DNNs' robustness to faults without any modification to the hardware. Experimental results show that the FAT methodology improves the tolerance to faults up to a factor 3.
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