Investigating the reliability impacts of neutron-induced soft errors in aerial image classification CNNs implemented in a softcore SRAM-based FPGA GPU

This work was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, CNPq, FAPERGS, and FINEP (Citar Project) and it has been partially supported by: MultiRad (PAI project funded by Région Auvergne-Rhône-Alpes) and IRT Nanoelec (ANR-10-AIRT-05 project funded by French PIA).International audienceThis work investigates the impacts of neutron-induced soft errors on the reliability of aerial image classification neural networks running on a softcore GPU implemented in an SRAM-based FPGA. We designed and trained fixed-point and floating-point all-convolutional neural networks to classify four-channel aerial images from the SAT-6 dataset, extracted from the U.S. National Agriculture Imagery Program, and implemented on FGPU, a configurable open-source GPU-like processor with floating-point arithmetic hardware. Results from fast neutron and thermal neutron irradiation experiments coupled with configuration bitstream fault injection campaigns show that the impact of soft errors in the aerial image classification must be taken care of with hardening techniques.