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Predicting soft errors on SRAM-based FPGAs without a wasteful time-consuming or a high-cost has always been a very difficult goal. Among the available methods, we proposed an updated version of analytical approach to predict Single Event Effects (SEEs) based on the analysis of the circuit the FPGA implements. In this paper, we provide an experimental validation of this approach, by comparing the results it provides with a fault injection campaign. We adopted our analytical method for computing the error-rate of a design implemented on SRAM-based FPGA. Furthermore, we compared the obtained soft-error figure with the one measured by fault injection. Experimental analysis demonstrated the analytical method closely match the effective soft-error rates becoming a viable solution for the soft-error estimation at early design phases.

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