A Design Space Exploration Framework for ANN-Based Fault Detection in Hardware Systems. (3rd December 2017)

• Record Type: Journal Article
• Authors: Savva, Andreas G.; Theocharides, Theocharis; Nicopoulos, Chrysostomos
• Other Names: Pai Ping Feng Academic Editor.
• Is Part Of: Journal of electrical and computer engineering. Volume 2017(2017)
• Publication Date: 2017-12-03
• Subjects: Computer engineering -- Periodicals; Electrical engineering -- Periodicals; 621.3905
• DOI: 10.1155/2017/9361493
• Languages: English
• ISSNs: 2090-0147

Abstract

Abstract : This work presents a design exploration framework for developing a high level Artificial Neural Network (ANN) for fault detection in hardware systems. ANNs can be used for fault detection purposes since they have excellent characteristics such as generalization capability, robustness, and fault tolerance. Designing an ANN in order to be used for fault detection purposes includes different parameters. Through this work, those parameters are presented and analyzed based on simulations. Moreover, after the development of the ANN, in order to evaluate it, a case study scenario based on Networks on Chip is used for detection of interrouter link faults. Simulation results with various synthetic traffic models show that the proposed work can detect up to 96–99% of interrouter link faults with a delay less than 60 cycles. Added to this, the size of the ANN is kept relatively small and they can be implemented in hardware easily. Synthesis results indicate an estimated amount of 0.0523 mW power consumption per neuron for the implemented ANN when computing a complete cycle.