Using Input-to-Output Masking for System-Level Vulnerability Estimation in High-Performance Processors
Alireza Haghdoost, Hossein Asadi, Amirali Baniasadi
In this paper, we enhance previously suggested
vulnerability estimation techniques by presenting a detailed
modeling technique based on Input-to-Output Masking (IOM).
Moreover we use our model to compute the System-level Vulnerability
Factor (SVF) for data-path components in a highperformance
processor. As we show, recent suggested estimation
techniques overlook the issue of error masking, mainly focusing
on time periods in which an error could potentially propagate
in the system. In this work we show that this is incomplete as it
ignores the masking impact. Our results show that including the
IOM factor can significantly affect the system-level vulnerability
for data-path components. As a case study, we analyze the IOM
factor for CPUs with different configurations. Our results show
that the average variation of the IOM factor is less than 5%.
Meantime, the IOM factor varies between 24% to 76% for the
applications studied here. Accordingly we find the IOM factor to
be less configuration dependent and mainly workload dependent.
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