Robustness of Artificial Neural Networks Based on Weight Alterations Used for Prediction Purposes

Abstract

Nowadays, due to their excellent prediction capabilities, the use of artificial neural networks (ANNs) in software has significantly increased. One of the most important aspects of ANNs is robustness. Most existing studies on robustness focus on adversarial attacks and complete redundancy schemes in ANNs. Such redundancy methods for robustness are not easily applicable in modern embedded systems. This work presents a study, based on simulations, about the robustness of ANNs used for prediction purposes based on weight alterations. We devise a method to increase the robustness of ANNs directly from ANN characteristics. By using this method, only the most important neurons/connections are replicated, keeping the additional hardware overheads to a minimum. For implementation and evaluation purposes, the networks-on-chip (NoC) case, which is the next generation of system-on-chip, was used as a case study. The proposed study/method was validated using simulations and can be used for larger and different types of networks and hardware due to its scalable nature. The simulation results obtained using different PARSEC (Princeton Application Repository for Shared-Memory Computers) benchmark suite traffic show that a high level of robustness can be achieved with minimum hardware requirements in comparison to other works.