Intrinsic Fault Tolerance of Hopfield Artificial Neural Network Model for Task Scheduling Technique in SoC

Rajhans Singh; Daniel Chillet

doi:10.5220/0005140902880293

Intrinsic Fault Tolerance of Hopfield Artificial Neural Network Model for Task Scheduling Technique in SoC

Rajhans Singh, Daniel Chillet

2014

Abstract

Due to the technology evolution, one of the main problems for future System-on-Chips (SoC) concerns the difficulties to produce circuits without defaults. While designers propose new structures able to correct the faults occurring during computation, this article addresses the control part of SoCs, and focuses on task scheduling for processors embedded in SoC. Indeed, to ensure the execution of application in presence of faults on such systems, operating system services will need to be fault tolerant. This is the case for the task scheduling service, which is an optimization problem that can be solved by Artificial Neural Network. In this context, this paper explores the intrinsic fault tolerance capability of Hopfield Artificial Neural Network (HANN). Our work shows that even if some neurons are in fault, a HANN can provide valid solutions for task scheduling problem. We define the intrinsic limit of fault tolerance capability of Hopfield model and illustrate the impact of fault on the network convergence.

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Paper Citation

in Harvard Style

Singh R. and Chillet D. (2014). Intrinsic Fault Tolerance of Hopfield Artificial Neural Network Model for Task Scheduling Technique in SoC . In Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014) ISBN 978-989-758-054-3, pages 288-293. DOI: 10.5220/0005140902880293

in Bibtex Style

@conference{ncta14,
author={Rajhans Singh and Daniel Chillet},
title={Intrinsic Fault Tolerance of Hopfield Artificial Neural Network Model for Task Scheduling Technique in SoC},
booktitle={Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)},
year={2014},
pages={288-293},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005140902880293},
isbn={978-989-758-054-3},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Neural Computation Theory and Applications - Volume 1: NCTA, (IJCCI 2014)
TI - Intrinsic Fault Tolerance of Hopfield Artificial Neural Network Model for Task Scheduling Technique in SoC
SN - 978-989-758-054-3
AU - Singh R.
AU - Chillet D.
PY - 2014
SP - 288
EP - 293
DO - 10.5220/0005140902880293