Radiation-Tolerant Deep Learning Processor Unit (DPU)-Based Platform Using Xilinx 20-nm Kintex UltraScale FPGA

This article presents a platform and design approach for enabling radiation-tolerant deep learning acceleration on static random access memory (SRAM)-based 20-nm Kintex UltraScale field-programmable gate arrays (FPGAs), for terrestrial and high-radiation environments. The presented platform is suitable for deep neural network (DNN) implementations with an emphasis on image classification and includes the solutions to mitigate both radiation-induced single-event functional interrupts (SEFIs) and network datapath corruptions. The radiation-tolerant deep learning platform combines Xilinx's deep learning processing unit (DPU) IP, triple modular redundancy (TMR) MicroBlaze soft processor IP, and soft error mitigation (SEM)-IP to mitigate SEFIs. Furthermore, a technique known as fault aware training (FAT) was applied to effectively mitigate single-event effects in the datapath. Test results from a high-energy proton beam (>60 MeV) experiment using the ResNet-18 convolutional neural network (CNN) for image classification are presented. The single-event upset (SEU) rate, system-level SEFI rate, and neural network classification/data-path performance are compared between the radiation-tolerant platform and a standard, nonmitigated approach. Results show that datapath classification errors dominate the system response (90%) versus SEFIs (10%). When compared to standard nonmitigated training techniques, the radiation-tolerant platform using FAT methods shows dramatic improvements in overall system response: the overall single-event cross section was reduced by half and 40% reduction in misclassification errors was observed. Also, datapath events with classification accuracy degradation larger than 5% were completely mitigated. The SEFI rate was reduced by 100x with implemented solutions and can be further reduced by optimizing the physical separation between TMR modules.

Automated summary

This article presents a platform and design approach for enabling radiation-tolerant deep learning acceleration. The platform includes solutions to mitigate radiation-induced interrupts and network datapath corruptions. Test results show significant improvements in system response and reduction in errors using the radiation-tolerant platform compared to standard nonmitigated approaches.