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Over past years, the easy accessibility to the large scale datasets hassignificantly shifted the paradigm for developing highly accurate predictionmodels that are driven from Neural Network (NN). These models can bepotentially impacted by the radiation-induced transient faults that might leadto the gradual downgrade of the long-running expected NN inference accelerator.The crucial observation from our rigorous vulnerability assessment on the NNinference accelerator demonstrates that the weights and activation functionsare unevenly susceptible to both single-event upset (SEU) and multi-bit upset(MBU), especially in the first five layers of our selected convolution neuralnetwork. In this paper, we present the relatively-accurate statistical modelsto delineate the impact of both undertaken SEU and MBU across layers and pereach layer of the selected NN. These models can be used for evaluating theerror-resiliency magnitude of NN topology before adopting them in thesafety-critical applications.

Entropy-Based Modeling for Estimating Soft Errors Impact on Binarized Neural Network Inference