Cost- and Dataset-free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators
Cost- and Dataset-free Stuck-at Fault Mitigation for ReRAM-based Deep Learning Accelerators
复制标题
基于 ReRAM 的深度学习加速器的无成本和无数据集卡住故障缓解
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
F. Kurdahi
中科院分区：
文献类型：
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作者：
Giju Jung;M. Fouda;Sugil Lee;Jongeun Lee;A. Eltawil;F. Kurdahi
电阻式RAM可以实现非常高效的矩阵向量乘法，引起了深度学习加速器研究的广泛关注。然而，高故障率是基于ReRAM crossbar阵列的深度学习加速器的根本挑战之一。在本文中，我们提出了一种无限制、无成本的方法来减轻ReRAM交叉阵列中固定故障对深度学习应用的影响。我们的技术利用了深度学习应用程序的统计特性，因此与以前的硬件或算法方法互补。我们在二进制网络中使用MNIST和CIFAR-10数据集的实验结果表明，我们的技术是非常有效的，无论是单独使用还是与以前的方法一起使用，错误率高达20%，高于以前的重新映射方法。我们还评估了我们的方法中存在的其他非理想因素，如变异性和IR下降。
Resistive RAMs can implement extremely efficient matrix vector multiplication, drawing much attention for deep learning accelerator research. However, high fault rate is one of the fundamental challenges of ReRAM crossbar array-based deep learning accelerators. In this paper we propose a dataset-free, cost-free method to mitigate the impact of stuck-at faults in ReRAM crossbar arrays for deep learning applications. Our technique exploits the statistical properties of deep learning applications, hence complementary to previous hardware or algorithmic methods. Our experimental results using MNIST and CIFAR-10 datasets in binary networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20 % fault rate, which is higher than the previous remapping methods. We also evaluate our method in the presence of other non-idealities such as variability and IR drop.