  The paper is proposing a methodology for modeling a gate-level netlist using
a Graph Convolutional Network (GCN). The model predicts the overall functional
de-rating factors of sequential elements of a given circuit. In the preliminary
phase of the work, the important goal is making a GCN which able to take a
gate-level netlist as input information after transforming it into the
Probabilistic Bayesian Graph in the form of Graph Modeling Language (GML). This
part enables the GCN to learn the structural information of netlist in graph
domains. In the second phase of the work, the modeled GCN trained with the a
functional de-rating factor of a very low number of individual sequential
elements (flip-flops). The third phase includes understanding of GCN models
accuracy to model an arbitrary circuit netlist. The designed model was
validated for two circuits. One is the IEEE 754 standard double precision
floating point adder and the second one is the 10-Gigabit Ethernet MAC
IEEE802.3 standard. The predicted results compared to the standard fault
injection campaign results of the error called Single EventUpset (SEU). The
validated results are graphically pictured in the form of the histogram and
sorted probabilities and evaluated with the Confidence Interval (CI) metric
between the predicted and simulated fault injection results.
