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

摘要

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. Area-Constrained 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.