Heavy Ions Testing of an All-Convolutional Neural Network for Image Classification Evolved by Genetic Algorithms and Implemented on SRAM-Based FPGA

BENEVENUTI, F.DE OLIVEIRA, A. B.LOPES, I. C.KASTENSMIDT, F. L.ADDED, N.AGUIAR, V. A P.MEDINA, N.H.Marcilei Aparecida Guazzelli2022-05-012022-05-012019-09-20BENEVENUTI, F.; DE OLIVEIRA, A. B.; LOPES, I. C.; KASTENSMIDT, F. L.; ADDED, N.; AGUIAR, V. A P.; MEDINA, N.H.; GUAZZELLI, M. A. Heavy Ions Testing of an All-Convolutional Neural Network for Image Classification Evolved by Genetic Algorithms and Implemented on SRAM-Based FPGA. 2019 19th European Conference on Radiation and Its Effects on Components and Systems, RADECS 2019, Sept. 2019.https://repositorio.fei.edu.br/handle/FEI/4485This work investigates the vulnerability of an image classification engine under heavy-ions accelerated irradiation. The engine is based on all-convolutional neural-network trained with the GTSRB traffic sign recognition benchmark and embedded into 28nm SRAM-based FPGA.Acesso RestritoHeavy Ions Testing of an All-Convolutional Neural Network for Image Classification Evolved by Genetic Algorithms and Implemented on SRAM-Based FPGAArtigo de evento10.1109/RADECS47380.2019.9745650deep learningneural networksreliabilitysingle-event effectsSRAM-based FPGAtraffic-sign recognition