# On the Reliability Assessment of Artificial Neural Networks Running on AI-Oriented MPSoCs
## 6 Conclusions  
This paper provides a methodology to improve the reliability of a neural computing system running in a multi-core device. Through the paper, it was shown that it is possible to identify the most critical neurons of a neural network and, based upon this, determine an optimal scheduling for an AI-oriented MPSoC. Following the proposed methodology, it was experimentally demonstrated that not all ANN neurons play the same role in the final task. It is fair to say that neural networks are equipped with more neurons than needed, but which neurons to remove is the focus of the class-oriented analysis. On the heels of this study, we presented a technique to identify the ANN's most critical neurons and sorted them according to their criticality. The experimental results show that our final sorting is more effective than those based only on a network-oriented analysis since we also consider the criticality of neurons with respect to the output classes. Relying on this analysis, the paper introduced an integer linear programming based mechanism, which takes into account the variance metric of portions of neurons. The aim was to uniformly distribute critical neurons to the available processing elements. The results of a further injection campaign at RTL provide evidence that the proposed scheduling can mask the effects of more faults and predict fewer wrong predictions. A reduction of 24.74% of wrong predictions and an improvement of 97.80% and 59.53% of masked faults was obtained. It is \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Fault Injection**} & \multicolumn{2}{c}{**Static Scheduling**} & \multicolumn{2}{c}{**Proposed Scheduling**} & \multirow{2}{*}{**[\%] Variation**} \\ \cline{2-2} \cline{4-6}  & **Images** & **[\%]** & & & \\ \hline SDC-1 & 1338 & 1.63 & 1007 & 1.23 & \(-\)24.74 \\ Hang & 71,840 & 87.61 & 65,040 & 79.32 & \(-\)9.47 \\ Masked, MSE \(>\) 0 & 4910 & 5.99 & 9712 & 11.84 & +97.80 \\ Masked, MSE \(=\) 0 & 3912 & 4.77 & 6241 & 7.61 & +59.53 \\ \hline Total & 82,000 & 100 & 82,000 & 100 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: RTL fault injection results: Evaluation of the effects of the same permanent faults on a CNN compiled with two different scheduling methods. worth saying that, without resorting to any redundancy-based technique (either software or hardware), the reliability of a NCS and its tolerance to faults can be improved. Future work will extend this analysis to deeper ANNs and different data sets. The reader should note that the adoption of the MNIST, SVHN, and CIFAR-10 data sets was consistent with the considered low-power and resource-constrained ASIC world. In the future, we will exploit deeper ANNs and more complex data sets, moving the target to GPUs and high-performance architectures or ad hoc hardware neural network chips. Clearly, with more complex neural networks, we need to study the feasibility of the uniform distribution and, if this cannot be satisfied, we will evaluate and propose different strategies that are well suited to deal with the complexity of the application. To conclude, in the future, we will address also other fault models, such as transient errors. Conceptualization, A.R. and E.S. ; methodology, A.R. and E.S. ; software, A.R. ; validation, A.R. and E.S. ; formal analysis, A.R. ; investigation, A.R. ; resources, E.S. ; data curation, A.R. ; writing--original draft preparation, A.R. ; writing--review and editing, A.R. and E.S. ; visualization, A.R. and E.S. ; supervision, E.S. ; project administration, A.R. and E.S. ; funding acquisition, E.S. Both authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: