Composing Graph Theory and Deep Neural Networks to Evaluate SEU Type Soft Error Effects
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Rapidly shrinking technology node and voltage scaling increase the susceptibility of Soft Errors in digital circuits. Soft Errors are radiation-induced effects while the radiation particles such as Alpha, Neutrons or Heavy Ions, interact with sensitive regions of microelectronic devices/circuits. The particle hit could be a glancing blow or a penetrating strike. A well apprehended and characterized way of analyzing soft error effects is the fault-injection campaign, but that typically acknowledged as time and resource-consuming simulation strategy. As an alternative to traditional fault injection-based methodologies and to explore the applicability of modern graph based neural network algorithms in the field of reliability modeling, this paper proposes a systematic framework that explores gate-level abstractions to extract and exploit relevant feature representations at low-dimensional vector space. The framework allows the extensive prediction analysis of SEU type soft error effects in a given circuit. A scalable and inductive type representation learning algorithm on graphs called GraphSAGE has been utilized for efficiently extracting structural features of the gate-level netlist, providing a valuable database to exercise a downstream machine learning or deep learning algorithm aiming at predicting fault propagation metrics. Functional Failure Rate (FFR): the predicted fault propagating metric of SEU type fault within the gate-level circuit abstraction of the 10-Gigabit Ethernet MAC (IEEE 802.3) standard circuit.
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