Atmospheric neutron single event effects for multiple convolutional neural networks based on 28-nm and 16-nm SoC

Abstract The single event effects (SEEs) evaluations caused by atmospheric neutrons were conducted on three different convolutional neural network (CNN) models (Yolov3, MNIST, and ResNet50) in the atmospheric neutron irradiation spectrometer (ANIS) at the China Spallation Neutron Source (CSNS). The Yolov3 and MNIST models were implemented on the XILINX 28-nm system-on-chip (SoC). Meanwhile, the Yolov3 and ResNet50 models were deployed on the XILINX 16-nm FinFET UltraScale+MPSoC. The atmospheric neutron SEEs on the tested CNN systems were comprehensively evaluated from six aspects, including chip type, network architecture, deployment methods, inference time, datasets, and the position of the anchor boxes. The various types of SEE soft errors, SEE cross-sections, and their distribution were analyzed to explore the radiation sensitivities and rules of 28-nm and 16-nm SoC. The current research can provide the technology support of radiation-resistant design of CNN system for developing and applying high-reliability, long-lifespan domestic artificial intelligence chips.