Heavy Ions Testing of an All-Convolutional Neural Network for Image Classification Evolved by Genetic Algorithms and Implemented on SRAM-Based FPGA
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2019-09-20
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BENEVENUTI, F.
DE OLIVEIRA, A. B.
LOPES, I. C.
KASTENSMIDT, F. L.
ADDED, N.
AGUIAR, V. A P.
MEDINA, N.H.
Marcilei Aparecida Guazzelli
DE OLIVEIRA, A. B.
LOPES, I. C.
KASTENSMIDT, F. L.
ADDED, N.
AGUIAR, V. A P.
MEDINA, N.H.
Marcilei Aparecida Guazzelli
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2019 19th European Conference on Radiation and Its Effects on Components and Systems, RADECS 2019
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BENEVENUTI, 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.
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This 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.