Although Resistive RAMs can support highly efficient matrix–vector multiplication, which is very useful for machine learning and other applications, the nonideal behavior of hardware, such as stuck-at fault (SAF) and IR drop is an important concern in making ReRAM crossbar array-based deep learning accelerators. Previous work has addressed the nonideality problem through either redundancy in hardware, which requires a permanent increase of hardware cost, or software retraining, which may be even more costly or unacceptable due to its need for a training dataset as well as high computation overhead. In this article, we propose a very lightweight method that can be applied on top of existing hardware or software solutions. Our method, called forward-parameter tuning (FPT), takes advantage of a certain statistical property existing in the activation data of neural network layers, and can mitigate the impact of mild nonidealities in ReRAM crossbar arrays (RCAs) for deep learning applications without using any hardware, a dataset, or gradient-based training. Our experimental results using MNIST, CIFAR-10, and CIFAR-100, and ImageNet datasets in binary and multibit networks demonstrate that our technique is very effective, both alone and together with previous methods, up to 20% fault rate, which is higher than even some of the previous remapping methods. We also evaluate our method in the presence of other nonidealities, such as variability and IR drop. Furthermore, we provide an analysis based on the concept of the effective fault rate (EFR), which not only demonstrates that EFR can be a useful tool to predict the accuracy of faulty RCA-based neural networks but also explains why mitigating the SAF problem is more difficult with multibit neural networks.

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.

Due to the inevitability of the IR drop problem in passive ReRAM crossbar arrays, finding a software solution that can predict the effect of IR drop without the need of expensive SPICE simulations, is very desirable. In this paper, two simple neural networks are proposed as software solution to predict the effect of IR drop. These networks can be easily integrated in any deep neural network framework to incorporate the IR drop problem during training. As an example, the proposed solution is integrated in BinaryNet framework and the test validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method. In addition, the proposed solution outperforms the prior work on challenging datasets such as CIFAR10 and SVHN.