Hardware Versus Software Fault Injection of Modern Undervolted SRAMs


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Researchers from Barcelona Supercomputing Center (Spain) and Abdullah Gul University in Kayseri (Turkey) are sharing an approach to apply real under-volting SRAM fault maps to a simulated system and observe the resiliency of the applications.
They compare the hardware guided fault injection approach with a random guided fault injection approach. Significant differences appears in the coarse categorization of the resiliency of the application, which become more obvious as the number of faulty bits increases. There are also differences when inspecting the quality of the output among the two techniques. This is because in an realisticsystem  not all fault locations have the same probability to  present faults, therefore from the software  perspective the faults can propagate to a limited number of software structures.

Abstract:
To improve power efficiency, researchers are experimenting with dynamically adjusting the supply voltage of systems below the nominal operating points. However, production systems are typically not allowed to function on voltage settings that is below the reliable limit. Consequently, existing software fault tolerance studies  are  based on fault  models, which inject faults on random fault locations using fault injection techniques.  
In this work we study whether random fault injection is accurate to simulate the  behavior of undervolted SRAMs. [...]
To compare random fault injection and hardware guided fault injection, we use two types of fault maps. The  first  type  of  maps  are  created  through  undervolting  real SRAMs and observing the location of the erroneous bits, whereas the second type of maps are created by corrupting random bits of  the  SRAMs.  During  our  study  we  corrupt  the  L1-D cache of the simulated system and we monitor the behavior of the two types of fault maps on the resiliency of six benchmarks. The difference among the resiliency of a benchmark when tested with the different fault maps can be up to 24%.

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