Abstract-Increasing chip power densities allied to the continuous technology shrink is making emerging multiprocessor embedded systems more vulnerable to soft errors. Due the high cost and design time inherent to board-based fault injection approaches, more appropriate and efficient simulation-based fault injection frameworks become crucial to guarantee the adequate design exploration support at early design phase. In this scenario, this paper proposes a fast and flexible fault injector framework, called OVPSim-FIM, which supports parallel simulation to boost up the fault injection process. Aiming at validating OVPSim-FIM, several fault injection campaigns were performed in ARM processors, considering a market leading RTOS and benchmarks with up to 10 billions of object code instructions. Results have shown that OVPSim-FIM enables to inject faults at speed of up to 10,000 MIPS, depending on the processor and the benchmark profile, enabling to identify erros and exceptions according to different criteria and classifications.
Virtual platform frameworks have been extended to allow earlier soft error analysis of more realistic multicore systems (i.e., real software stacks, state-of-the-art ISAs). The high observability and simulation performance of underlying frameworks enable to generate and collect more error/failurerelated data, considering complex software stack configurations, in a reasonable time. When dealing with sizeable failure-related data sets obtained from multiple fault campaigns, it is essential to filter out parameters (i.e., features) without a direct relationship with the system soft error analysis. In this regard, this paper proposes the use of supervised and unsupervised machine learning techniques, aiming to eliminate non-relevant information as well as identify the correlation between fault injection results and application and platform characteristics. This novel approach provides engineers with appropriate means that able are able to investigate new and more efficient fault mitigation techniques. The underlying approach is validated with an extensive data set gathered from more than 1.2 million fault injections, comprising several benchmarks, a Linux OS and parallelization libraries (e.g., MPI, OpenMP), as well as through a realistic automotive case study.
Sistemas eletrônicos estão integrando processadores multicore e GPUs (Graphical Processing Unit) com o objetivo de executar configurações complexas de aplicações. É esperado que esses sistemas sofram com pelo menos uma falha por dia nos próximos anos, o que pode levar a erros que coloquem a vida de outros em perigo. Esse trabalho apresenta quatro novas técnicas de injeção de falhas que possibilitam um maior controle e analise do comportamento de sistemas multicore. Essas técnicas são não intrusivas, isto é, não precisamos modificar o software ou o hardware, e são integradas em um framework para execução automática. Para validá-las, nos utilizamos aplicações reais com ate 43 bilhões de instruções. Resultados inicias mostram que isolar partes criticas de uma aplicação pode levar a uma analise de soft-error mais eficiente, ou seja, reduzir o numero de falhas mascaradas em até 28%. Falhas mascaradas não proveem informação sobre o que aconteceria com o sistema, mas sim a probabilidade de um erro ocorrer.
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