Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional Input
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...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | International Journal of Reconfigurable Computing |
| Online Access: | http://dx.doi.org/10.1155/2014/279673 |
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| _version_ | 1832565146631798784 |
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| author | Naveed Imran Ronald F. DeMara |
| author_facet | Naveed Imran Ronald F. DeMara |
| author_sort | Naveed Imran |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1af39122b5664cd58d0c222782d0a57e |
| institution | Kabale University |
| issn | 1687-7195 1687-7209 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Reconfigurable Computing |
| spelling | doaj-art-1af39122b5664cd58d0c222782d0a57e2025-02-03T01:09:09ZengWileyInternational Journal of Reconfigurable Computing1687-71951687-72092014-01-01201410.1155/2014/279673279673Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional InputNaveed Imran0Ronald F. DeMara1Department of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USADepartment of Electrical Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USADistance-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.http://dx.doi.org/10.1155/2014/279673 |
| spellingShingle | Naveed Imran Ronald F. DeMara Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional Input International Journal of Reconfigurable Computing |
| title | Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional Input |
| title_full | Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional Input |
| title_fullStr | Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional Input |
| title_full_unstemmed | Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional Input |
| title_short | Distance-Ranked Fault Identification of Reconfigurable Hardware Bitstreams via Functional Input |
| title_sort | distance ranked fault identification of reconfigurable hardware bitstreams via functional input |
| url | http://dx.doi.org/10.1155/2014/279673 |
| work_keys_str_mv | AT naveedimran distancerankedfaultidentificationofreconfigurablehardwarebitstreamsviafunctionalinput AT ronaldfdemara distancerankedfaultidentificationofreconfigurablehardwarebitstreamsviafunctionalinput |