Title

GPU Acceleration of Genetic Algorithms for Subset Selection for Partial Fault Tolerance

Document Type

Conference Proceeding

Publication Date

2012

Publication Title

Proceedings of the 18th Annual International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA)

Conference Name

International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA)

Abstract

As reconfigurable logic devices see increasing use in aerospace and terrestrial applications, fault tolerant techniques are being developed to counter rising susceptibility due to decreasing feature sizes. Applying fault-tolerance to an entire circuit induces unacceptable area and time penalties, thus some techniques trade area for fault tolerance. AreaConstrained Partial Fault Tolerance (ACPFT) is a methodology that explicitly accepts a device’s resources as an input and attempts to find a maximally fault-tolerant subset, but determining an optimal partition is still an open problem. While ACPFT originally used heuristics for subset selection, a modification called ACPFT-GA has been developed that uses genetic evolution to provide significantly better fault coverage in many applications. However, its running time is substantially longer than standard ACPFT and may be prohibitive. This paper presents a GPU-accelerated version of ACPFT-GA that has executed over 27 times faster than CPU versions, allowing ACPFT-GA to better scale to larger circuits.

Comments

pp. 10-16

Rights Statement

© 2011 WorldComp

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