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International Journal of Reconfigurable Computing
Volume 2014, Article ID 279673, 21 pages
Research Article

Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional Input

Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA

Received 29 September 2013; Revised 26 December 2013; Accepted 9 January 2014; Published 17 March 2014

Academic Editor: Walter Stechele

Copyright © 2014 Naveed Imran and Ronald F. DeMara. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Distance-Ranked Fault Identification (DRFI) is a dynamic reconfiguration technique which employs runtime inputs to conduct online functional testing of fielded FPGA logic and interconnect resources without test vectors. At design time, a diverse set of functionally identical bitstream configurations are created which utilize alternate hardware resources in the FPGA fabric. An ordering is imposed on the configuration pool as updated by the PageRank indexing precedence. The configurations which utilize permanently damaged resources and hence manifest discrepant outputs, receive lower rank are thus less preferred for instantiation on the FPGA. Results indicate accurate identification of fault-free configurations in a pool of pregenerated bitstreams with a low number of reconfigurations and input evaluations. For MCNC benchmark circuits, the observed reduction in input evaluations is up to 75% when comparing the DRFI technique to unguided evaluation. The DRFI diagnosis method is seen to isolate all 14 healthy configurations from a pool of 100 pregenerated configurations, and thereby offering a 100% isolation accuracy provided the fault-free configurations exist in the design pool. When a complete recovery is not feasible, graceful degradation may be realized which is demonstrated by the PSNR improvement of images processed in a video encoder case study.