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FPGA Based Fault Tolerance and Recovery Process in Digital Systems Using Genetic Algorithm

Authors: P. Renugadevi; R. Jeyanthi;

FPGA Based Fault Tolerance and Recovery Process in Digital Systems Using Genetic Algorithm

Abstract

In recent times self-repairing digital systems have emerged as the most favorable alternative for fault-tolerant systems. However, such systems are still unrealistic in many cases, predominantly due to the complex rerouting process that follows cell replacement. They lose efficiency when the circuit size surges, due to extra hardware besides the functional circuit and the non-utilization of normal operating hardware for fault recovery. In Endocrine cellular communication, when an endocrine cell dies in a specific process, the collection of cells and tissues of an organism secretes a hormone and its connections are maintained through blood vessels. Inspired by this communication process, a system has been proposed which reduces hardware overhead to maintain good fault coverage. A fault recovering system is proposed by the use of genetic algorithm to increase the lifetime of the digital circuits. Genetic Algorithms are often expected to design combinational circuits with the fault-tolerant and self-repair ability, Spare cells are used as a replacement in case of failures occurring in working cells. Comparing with the existing methods, the mechanism proposed will be efficient for the real fault tolerant systems.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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