  The great quest for adopting AI-based computation for
safety-/mission-critical applications motivates the interest towards methods
for assessing the robustness of the application w.r.t. not only its
training/tuning but also errors due to faults, in particular soft errors,
affecting the underlying hardware. Two strategies exist: architecture-level
fault injection and application-level functional error simulation. We present a
framework for the reliability analysis of Convolutional Neural Networks (CNNs)
via an error simulation engine that exploits a set of validated error models
extracted from a detailed fault injection campaign. These error models are
defined based on the corruption patterns of the output of the CNN operators
induced by faults and bridge the gap between fault injection and error
simulation, exploiting the advantages of both approaches. We compared our
methodology against SASSIFI for the accuracy of functional error simulation
w.r.t. fault injection, and against TensorFI in terms of speedup for the error
simulation strategy. Experimental results show that our methodology achieves
about 99\% accuracy of the fault effects w.r.t. SASSIFI, and a speedup ranging
from 44x up to 63x w.r.t. TensorFI, that only implements a limited set of error
models.
