# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 6 Discussion
Subsequently, Adaptive Clipper has a similar EoI score on Mobilenet V2 and on Resnet18 and Squeezenet (6/10, 5/10, and 6/10, respectively) but with different ITO (2.24%, 0.30%, and 0.88%, respectively) due to the higher number of activation functions in Mobilenet V2 architecture with respect to the others. However, Adaptive Clipper improves the reliability of all considered architectures by reducing the RAD by 9.24%, 15.31%, and 21.48%, respectively, against WSBF, and by 7.67%, 7.65%, and 29.48  respectively, against NBER. The paper that originally introduced Ranger tested this hardening technique on a wide selection of neural networks (eight architectures) and against the two error models that mimic the effect of transient faults. Their evaluations show the effectiveness of Ranger when it is implemented on all the selected NN architectures. Similarly, our evaluations, using hardware-aware error models, demonstrate the effectiveness of Ranger when it is implemented on any of the considered NNs (five architectures). In general, Ranger always has an EoI score that never goes above 3/10, mainly due to the absence of the training step. At the same time, as Ranger introduces an overhead based on the total number of layers, it never has more than 3% of overhead except for Mnasnet, which is the NN with the highest number of layers, thus, according to the implementation of Ranger, being the NN with the highest overhead (7.54%) with respect to all NN architectures. Despite the low HT impact evaluated in terms of EoI and ITO, Ranger failed to enhance the reliability, scoring a slight decrease in RAD of 0.05% against WSBF and 4.09% against NBER. On the other hand, Ranger effectively enhances the reliability of Lenet5, Resnet18, and Squeezenet, decreasing the RAD of 27.84%, 14.77%, and 17.39% during WSBF and 21.63%, 9.14%, and 24.84% during NBER. Swap ReLU was originally tested on one NN architecture (i.e., Resnet44) and trained and tested on two different datasets (i.e., Cifar10 and Cifar100) against the effect of transient faults, significantly reducing the critical SDC percentage. However, in our evaluations, despite the highest EoI with respect to the other NN architectures (7/10), Swap ReLU6 introduces a limited introduced overhead (0.29%) and a decrease in RAD of 16.58% during WSBF and of 13.29% during NBER. On the other hand, when Swap ReLU6 is implemented on Mobilenet V2, it introduced an increased overhead (2.29%) with a similar EoI (6/10), and during the WSBF, it reduced the impact on the accuracy by 5.55% with respect to the original NN architecture, but the RAD increased by 14.27% during NBER FI. Similarly, when Swap ReLU6 is implemented on Resnet18, it achieves 6/10 of the EoI score with an introduced ITO of 0.30%. Still, the RAD increased by 10.46% with respect to the NN architecture without HT implemented during WSBF FI while the RAD is decreased by 10.55% during the NBER FI. Indeed, suppose Swap ReLU is implemented on Squeezenet. In that case, it achieves 3/10 of the EoI score with 0.88% of the introduced overhead, and the new NN architecture shows a limited enhanced resiliency against WSBF and NBER of 0.6% and 1.17%, respectively. Median filter and fine-grain TMR were initially evaluated on three NN architectures trained and tested on three datasets with application-level FI campaigns and a completely hardware-agnostic error model. The employed error model involves the injection of noise in neurons and weights in two different FI campaigns, while the third FI campaign simultaneously corrupts weights and neurons with random noise. In general, median filter has a high EoI score of 10/10 and the highest introduced overhead on Mnasnet and Mobilenet V2 (of 11.7% and 10.2%), and it failed the convergence of the training algorithm in 3 out of the 5 tested NN architectures (Mnasnet, MobilenetV2, and Squeezenet). Moreover, when it is implemented on Resnet18, the achieved RAD increases from 18.92% (without the HT implemented) to 26.82%.