On Improvement in Fault Tolerance of Hopfield Neural Networks

Kamiura, Naotake; Isokawa, Teijiro; Matsui, Nobuyuki

doi:10.1109/ats.2004.64

Cited by 1 publication

(1 citation statement)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adding fault tolerance constraints during the training also help in improving the global behavior of the neural network. The paper (Tchernev et al, 2005) shows how improving the fault tolerance of a feed forward ANN for optimization problem by modifying the training method of the network In (Kamiura et al, 2004), the authors propose to define the network by taking into account of faults occurring. Single-fault injection and double-fault injection are used for learning schemes and the authors demonstrate the the fault tolerance is improved.…”

Section: Introductionmentioning

confidence: 99%

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

Singh

Chillet

2014

Proceedings of the International Conference on Neural Computation Theory and Applications

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%