# Comparing Application-Level Hardening Techniques for Neural Networks on GPUs
## 6 Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{_HT Impact_} & \multicolumn{3}{c}{_Faultly Scenario_} \\ \cline{3-8}  & & & & \multicolumn{3}{c}{_WSBF_} & \multicolumn{3}{c}{_NBER_} \\ \cline{3-8}
**HT** & **NN** & **EoI** & **ITO** & **W** & **W/O** & **W** & **W/O** \\ \hline \multirow{6}{*}{**AC**} & Lenet5 & 4/10 & 0.29\% & 9.75\% & 39.06\% & 6.80\% & 28.49\% \\ \cline{2-8}  & Mnasnet & 6/10 & 3.52\% & 41.64\% & 47.91\% & 53.90\% & 51.75\% \\ \cline{2-8}  & Moobilenet V2 & 6/10 & 2.24\% & 7.44\% & 16.68\% & 16.68\% & 24.35\% \\ \cline{2-8}  & Resnet18 & 5/10 & 0.30\% & 24.22\% & 39.53\% & 11.27\% & 18.92\% \\ \cline{2-8}  & Squeezenet & 6/10 & 0.88\% & 20.64\% & 42.12\% & 8.46\% & 37.94\% \\ \hline \multirow{6}{*}{**R**} & Lenet5 & 1/10 & 2.29\% & 11.22\% & 39.06\% & 6.86\% & 28.49\% \\ \cline{2-8}  & Mnasnet & 3/10 & 7.54\% & 38.93\% & 47.91\% & 52.94\% & 51.75\% \\ \cline{2-8}  & Moobilenet V2 & 3/10 & 1.93\% & 16.63\% & 16.68\% & 20.26\% & 24.35\% \\ \cline{2-8}  & Resnet18 & 2/10 & 0.65\% & 24.76\% & 39.53\% & 9.78\% & 18.92\% \\ \cline{2-8}  & Squeezenet & 3/10 & 2.16\% & 24.73\% & 42.12\% & 13.10\% & 37.94\% \\ \hline \multirow{6}{*}{**SR**} & Lenet5 & 7/10 & 0.29\% & 22.48\% & 39.06\% & 15.20\% & 28.49\% \\ \cline{2-8}  & Mnasnet & 6/10 & 2.50\% & - & - & - & - \\ \cline{2-8}  & Moobilenet V2 & 6/10 & 2.24\% & 11.13\% & 16.68\% & 38.62\% & 24.35\% \\ \cline{2-8}  & Resnet18 & 6/10 & 0.30\% & 49.99\% & 39.53\% & 8.37\% & 18.92\% \\ \cline{2-8}  & Squeezenet & 3/10 & 0.88\% & 41.55\% & 42.12\% & 36.77\% & 37.94\% \\ \hline \multirow{6}{*}{**MF**} & Lenet5 & 10/10 & 2.29\% & 17.72\% & 39.06\% & 7.75\% & 28.49\% \\ \cline{2-8}  & Mnasnet & 10/10 & 11.7\% & - & - & - & - \\ \cline{2-8}  & Moobilenet V2 & 10/10 & 10.2\% & - & - & - & - \\ \cline{2-8}  & Resnet18 & 10/10 & 0.65\% & 15.46\% & 39.53\% & 26.82\% & 18.92\% \\ \cline{2-8}  & Squeezenet & 10/10 & 3.4\% & - & - & - & - \\ \hline \multirow{6}{*}{**Fine-grain**} & Lenet5 & 5/10 & 200\% & 0\% & 39.06\% & 23.09\% & 28.49\% \\ \cline{2-8}  & Mnasnet & 5/10 & 200\% & 0\% & 47.91\% & 40.11\% & 51.75\% \\ \cline{2-8}  & Moobilenet V2 & 5/10 & 200\% & 0\% & 16.68\% & 11.85\% & 24.35\% \\ \cline{2-8}  & Resnet18 & 5/10 & 200\% & 0\% & 39.53\% & 11.89\% & 18.92\% \\ \cline{2-8}  & Squeezenet & 5/10 & 200\% & 0\% & 42.12\% & 37.36\% & 37.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Overview of the achieved results per hardening technique for each neural network architecture. This table reports the results of the analysis conducted on the (i) HT implementation impact and (iii) the HT effectiveness in the faulty scenario. The HT implementation impact is evaluated with the EoI and ITO metrics. On the other hand, the reliability enhancement in terms of RAD is evaluated during WSBF and NBER FI campaigns. To directly see the effect of the HT in terms of reliability enhancement, the RAD of the NN _with (W)_ the HT implemented is compared to the RAD of the NN _without (W/O)_ the HT implemented. The empty cells correspond to the FI campaigns that were not performed due to the failed training process during the implementation phase. In addition, the red cells indicate an increase in RAD with respect to the tested NN when the HT is implemented with respect to the original NN architecture without the HT implemented. In this table, the HT names are abbreviated as follows: Baseline (BL), Adaptive Clipper (AC), Swap ReLU6 (SR), and median filter (MF). of 21.69%. Although the EoI score of Adaptive Clipper remains limited (6/10), as well as the ITO (3.52%) when it is implemented on Mnasnet, it is not able to counteract the effect of errors during NBER FI, leading to an increased RAD of 2.25%. On the other hand, the RAD is decreased by 6.27% against WSBF FI.