VANDOR: Mitigating SEUs into Quantized Neural Networks
Wilfread Guillemé, Angeliki S. Kritikakou, Youri Helen, Cédric Killian, Daniel Chillet · 2024
Embedded neural networks are increasingly deployed in critical applications, such as avionics and autonomous vehicle control. However, their reliability is challenged by various sources of soft errors, including radiation-induced faults from cosmic ray strikes, leading to Single Event Upsets (SEUs). To ensure the reliability of such systems, we present a novel hardware-based fault protection strategy tailored for embedded neural networks. The idea is based on mitigating faults by adapting at run-time any erroneous values (parameters, intermediate data) due to SEU towards zero upon fault detection. As neural networks exhibit heterogeneous sensitivity to fault direction, our hardware-based approach triplicates the sign bit (TMR) and uses a Voter block based on logical AND/OR gates to handle fault directionality. Through a comprehensive and exhaustive fault injection study, conducted on a Convolutional Neural Network (CNN) model, implemented on FPGA using fixed-point quantization, we show that our method is applicable to various hardware architectures while optimizing hardware cost, a crucial aspect in the context of embedded systems. Obtained results show that VANDOR protection efficiency is near ${9 0 . 9 7 \%}$ for the LeNet-5 CNN inference model, suitable for an embedded system. Additionally, it significantly reduces area overhead compared to existing approaches.