# Robustness of Artificial Neural Networks Based on Weight Alterations Used for Prediction Purposes
## 1 Introduction  
Neural networks yield excellent prediction results if appropriately trained for use in different application domains [1, 2]. They have the power to extract valuable information from complex data and predict trends that cannot be easily detected by other mechanisms. While neural networks have certain prediction capabilities, their accuracy decreases in the presence of small perturbations. This makes it difficult to apply them in critical areas [3].  
One of the main research goals of the analysis of robustness is to propose different solutions/architectures with increased robustness [4]. This is one of the fundamental problems that require extensive future research since ANN faults significantly affect the accuracy and reliability of these types of networks.  
One of the most well-known solutions for robustness improvement and fault tolerance is to apply triple/dual modular redundancy (n-MR schemes) [5], replicating the entire ANN architecture, but these methods are very restrictive due to additional hardware overheads. This redundancy includes full replications of ANN architecture and is not applicable to modern on-chip systems due to area limitations [6].  
Additionally, the robustness of neural networks to adversarial attacks is critical due to security issues that make these neural networks vulnerable [7, 8], thus causing poor performance and accuracy. Until recently, researchers concentrated on comparing results based on having/not having adversarial machine learning attacks and providing different solutions. Defenses based on adversarial training have been proposed, but these defenses are often defeated by stronger attacks [8].  
Motivated by the above, we focused on the property of robustness of neural networks used for prediction purposes based on weight alterations. Therefore, we examined how the prediction accuracy of ANNs is impacted by weight faults. The networks-on-chip case, which was presented in the framework of one of our previous studies [1], was used as a case study to evaluate the robustness of the ANNs based on simulations. The goal of this work is to discuss the robustness of neural networks to changes in weights that might affect the prediction results. This work discusses and analyzes weight alterations in ANNs based on simulations/implementations and provides a protection/robustness method for ANNs. This method will help to maintain high robustness in ANNs with minimum additional hardware overheads. In this work, the architecture of the ANNs was changed by duplicating only the most important neurons/connections in order to achieve good prediction accuracy for ANNs weight faults. We analyzed the importance of neurons/connections in ANNs based on actual simulations/implementations and how the prediction accuracy of the ANNs is affected in cases of weight faults.  
The rest of this paper is organized as follows. Section 2 describes the background and related research. In Section 3, we introduce the methodology and a robust approach for detecting weight faults in ANNs with the support of simulation results and analysis. Section 4 offers a brief conclusion to the paper.