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

• Published in: Microelectronics and reliability 2022-11, Vol.138, p.114738, Article 114738
• Main Authors: Benevenuti, Fabio, Gonçalves, Marcio M., Pereira, Evaldo Carlos F., Vaz, Rafael G., Gonçalez, Odair L., Bastos, Rodrigo Possamai, Letiche, Manon, Kastensmidt, Fernanda L., Azambuja, José Rodrigo
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Engineering Sciences; Graphics processing units; Micro and nanotechnologies; Microelectronics; Programmable logic; Radiation effects
• ISSN: 0026-2714; 1872-941X
• DOI: 10.1016/j.microrel.2022.114738

This 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. •SRAM-based FPGA are sensitive to radiation-induced transient effects•CNN applications must be hardened to mitigate fault accumulation•Experimental irradiation results showed consistency with emulated ones•Designer should focus hardening strategies on interconnection and control structures