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

Rajhans Singh, Daniel Chillet

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.

References

  1. Bolt, G. R. (1992). Fault tolerance in artificial neural networks: are neural networks inherently fault tolerant?. PhD thesis, University of York.
  2. Cardeira, C. and Mammeri, Z. (1995). Preemptive and nonpreemptive real-time scheduling based on neural networks. Proceedings DDCS95, pages 67-72.
  3. Chillet, D., Eiche, A., Pillement, S., and Sentieys, O. (2011). Real-time scheduling on heterogeneous system-on-chip architectures using an optimised artificial neural network. Journal of Systems Architecture, 57:340-353.
  4. Chillet, D., Pillement, S., and Sentieys, O. (2010). Algorithm-Architecture Matching for Signal and Image Processing, Springer, volume 73 of Lecture Notes in Electrical Engineering, chapter RANN: A Reconfigurable Artificial Neural Network Model for Task Scheduling on Reconfigurable System-on-Chip, pages 117-144. Springer Netherlands.
  5. Hopfield, J. J. (1984). Neurons with graded response have collective computational properties like those of twostate neurons. Proceedings of the national academy of sciences, 81(10):3088-3092.
  6. Hopfield, J. J. and Tank, D. W. (1985). neural computation of decisions in optimization problems. Biological cybernetics, 52(3):141-152.
  7. Kamiura, N., Isokawa, T., and Matsui, N. (2004). On improvement in fault tolerance of hopfield neural networks. In Test Symposium, 2004. 13th Asian, pages 406-411.
  8. Protzel, P. W., Palumbo, D. L., and Arras, M. K. (1993). Performance and fault-tolerance of neural networks for optimization. Neural Networks, IEEE Transactions on, 4(4):600-614.
  9. Tchernev, E. B., Mulvaney, R. G., and Phatak, D. S. (2005). Investigating the fault tolerance of neural networks. Neural computation, 17(7):1646-1664.
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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