| dc.contributor.author | BENEVENUTI, F. | |
| dc.contributor.author | DE OLIVEIRA, A. B. | |
| dc.contributor.author | LOPES, I. C. | |
| dc.contributor.author | KASTENSMIDT, F. L. | |
| dc.contributor.author | ADDED, N. | |
| dc.contributor.author | AGUIAR, V. A P. | |
| dc.contributor.author | MEDINA, N.H. | |
| dc.contributor.author | Marcilei Aparecida Guazzelli | |
| dc.date.accessioned | 2022-05-01T06:03:59Z | |
| dc.date.accessioned | 2024-02-27T16:29:30Z | |
| dc.date.available | 2022-05-01T06:03:59Z | |
| dc.date.available | 2024-02-27T16:29:30Z | |
| dc.date.issued | 2019-09-20 | |
| dc.identifier.citation | 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. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12032/122122 | |
| dc.description.abstract | 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. | |
| dc.relation.ispartof | 2019 19th European Conference on Radiation and Its Effects on Components and Systems, RADECS 2019 | |
| dc.rights | Acesso Restrito | |
| dc.title | Heavy Ions Testing of an All-Convolutional Neural Network for Image Classification Evolved by Genetic Algorithms and Implemented on SRAM-Based FPGA | |
| dc.type | Artigo de evento | |
