基于ReRAM横梁的CNN可靠训练动态任务映射

C. Tung, Biresh Kumar Joardar, P. Pande, J. Doppa, Hai Helen Li, K. Chakrabarty
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引用次数: 0

摘要

一种基于ReRAM交叉栏的计算系统(RCS)可以加速CNN的训练。然而,由于制造缺陷和有限的耐用性导致的硬件故障阻碍了RCS的广泛采用。提出了一种基于动态任务重映射的CNN故障训练方法。实验结果表明,使用CIFAR-IO、CIFAR-100和SVHN数据集训练vgg、ResNets和SqueezeNet等流行的cnn时,在存在故障的情况下,所提出的低开销方法平均准确率损失仅为0.85%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dynamic Task Remapping for Reliable CNN Training on ReRAM Crossbars
A ReRAM crossbar-based computing system (RCS) can accelerate CNN training. However, hardware faults due to manufacturing defects and limited endurance impede the widespread adoption of RCS. We propose a dynamic task remapping-based technique for reliable CNN training on faulty RCS. Experimental results demonstrate that the proposed low-overhead method incurs only 0.85% accuracy loss on average while training popular CNNs such as VGGs, ResNets, and SqueezeNet with the CIFAR-IO, CIFAR-100, and SVHN datasets in the presence of faults.
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