  Neural Networks (NNs) are increasingly used in the last decade in several
demanding applications, such as object detection and classification, autonomous
driving, etc. Among different computing platforms for implementing NNs, FPGAs
have multiple advantages due to design flexibility and high performance-to-watt
ratio. Moreover, approximation techniques, such as quantization, have been
introduced, which reduce the computational and storage requirements, thus
enabling the integration of larger NNs into FPGA devices. On the other hand,
FPGAs are sensitive to radiation-induced Single Event Upsets (SEUs). In this
work, we perform an in-depth reliability analysis in an FPGA-based Binarized
Fully Connected Neural Network (BNN) accelerator running a statistical fault
injection campaign. The BNN benchmark has been produced by FINN, an open-source
framework that provides an end-to-end flow from abstract level to design,
making it easy to design customized FPGA NN accelerators, while it also
supports various approximation techniques. The campaign includes the injection
of faults in the configuration memory of a state-of-the-art Xilinx Ultrascale+
FPGA running the BNN, as well an exhaustive fault injection in the user flip
flops. We have analyzed the fault injection results characterizing the SEU
vulnerability of the circuit per network layer, per clock cycle, and register.
In general, the results show that the BNNs are inherently resilient to soft
errors, since a low portion of SEUs in the configuration memory and the flip
flops, cause system crashes or misclassification errors.
