Early Analysis of Soft Error Effects for Aerospace Applications Using Probabilistic Model Checking

SRAM-based FPGAs are increasingly popular in the aerospace industry for their field programmability and low cost. However, they suffer from cosmic radiation induced Single Event Upsets (SEUs), commonly known as soft errors. In safety-critical applications, the dependability of the design is a prime concern since failures may have catastrophic consequences. An early analysis of dependability and performance of such safety-critical applications can reduce the design effort and increases the confidence. This paper introduces a novel methodology based on probabilistic model checking, to analyze the dependability and performability properties of safety-critical systems for early design decisions. Starting from a high-level description of a model, a Markov reward model is constructed from the Control Data Flow Graph (CDFG) of the system and a component characterization library targeting FPGAs. Such an exhaustive model captures all the failures and repairs possible in the system within the radiation environment. We present a case study based on a benchmark circuit to illustrate the applicability of the proposed approach and to demonstrate that a wide range of useful dependability and performability properties can be analyzed using our proposed methodology.