DelayAVF: Calculating Architectural Vulnerability Factors for Delay Faults
Peter W. Deutsch, Vincent Quentin Ulitzsch, Sudhanva Gurumurthi, Vilas Sridharan, Joel Emer, Mengjia Yan · 2024
Reliability is a key design consideration for modern microprocessors. A surge of reports from major cloud vendors describing new silent data corruption (SDC) behaviours at scale suggest a recent change in the nature of faults in the wild. Recent publications have suggested that one root cause of these SDCs may be small delay faults (SDFs) induced by marginal defects that increase a circuit's propagation time by a small (sub-cycle) delay. Reasoning about the effects of these faults early in the design of a processor is thus of increasing importance for reliability at-scale. Computer architects currently reason about the resilience of microarchitectures against particle strike induced faults using Architectural Vulnerability Factor (AVF) which describes the probability that a particle strike impacting a particular microar-chitectural structure results in a program-visible failure. In this paper, we develop an AVF-like metric to quantify a processor's vulnerability to SDFs. We conduct a systematic analysis of the potential impacts of SDFs and determine that particle strike AVF is insufficient to reason about SDFs. Considering SDFs requires additional reasoning about the timing characteristics of a circuit, the state element(s) that experience an error due to a fault, and whether the resulting state element errors cause a program-visible failure. In this paper we present DelayAVF, a metric that quantifies microarchitectural vulnerability to small delay faults. We develop a two-step methodology to analyze the DelayAVF of a hardware design. We then analyze the DelayAVF of an open-source RISC-V core, finding new architectural reliability insights that do not present themselves through traditional AVF analysis. Finally, we provide approximations for DelayAVF that allow for the reuse of particle strike AVF data (for instance, from existing fault injection studies).