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We propose AVGI, a new Statistical Fault Injection (SFI)-based methodology, which delivers orders of magnitude faster assessment of the Architectural Vulnerability Factor (AVF) of a microprocessor chip, while retaining the high accuracy of SFI. The proposed methodology is based on three key insights about the way that faults traverse complex out-of-order microarchitectures: (1) the distribution of the different ways that hardware faults manifest at the software (i.e., the first effects of faults to the software layer) is relatively uniform across workloads, (2) the final effects of faults in a specific hardware structure (i.e., their effect on the program execution) is relatively uniform for different workloads and depends on the distribution of the above fault manifestations, and (3) the majority of first manifestations occur in certain timeframe from the fault occurrence, which is significantly shorter than the complete execution of the …