Abstract
Radiation-induced soft errors are a major reliability concern in circuits fabricated at advanced technology nodes. Online soft-error vulnerability estimation offers the flexibility of exploiting dynamic fault-tolerant mechanisms for cost-effective reliability enhancement. We propose a generic run-time method with low area and power overhead to predict the soft-error vulnerability of on-chip memory arrays as well as logic cores. The vulnerability prediction is based on signal probabilities (SPs) of a small set of flip-flops, chosen at design time, by studying the correlation between the soft-error vulnerability and the flip-flop SPs for representative workloads. We exploit machine learning to develop a predictive model that can be deployed in the system in software form. Simulation results on two processor designs show that the proposed technique can accurately estimate the soft-error vulnerability of on-chip logic core, such as sequential pipeline logic and functional units as well as memory arrays that constitute the instruction cache, the data cache, and the register file.
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