Assessment of Multicore Processor Soft Error Reliability Using BBRO ‐ DNN and SSF ‐ FIS Models

ABSTRACT The development of virtual platform frameworks has made it possible to perform early soft error analysis of more realistic multicore systems, that is, real software stacks and state‐of‐the‐art ISAs. Because of the underlying frameworks' strong observability and simulation performance, more error/failure related data may be generated and collected in a reasonable amount of time even with complicated software stack setups. Parameters (i.e., features) that do not directly connect to the system soft error analysis must be filtered away when working with sizable failure‐related data sets that come from several fault campaigns. In this regard, the paper proposes an assessment of multicore processor soft error reliability using BBRO‐DNN and SSF‐FIS models. At first, source code is converted into the executable code using LLVM compiler and applied over the Gem 5 virtual platform. Then, faults are injected into the fault injection module of the virtual platform. Profiling module analysis the faults and the reaction of the system and submits the report. The fault report is given into the proposed BBRO‐DNN model for classifying the fault type. Finally, the system's reliability is evaluated using classified fault type. Experimental results are done by comparing the proposed and existing models to show the superiority of the developed model.