Modeling the Effect of SEUs on the Configuration Memory of SRAM-FPGA based CNN Accelerators

Convolutional Neural Networks (CNNs) are widely used in computer vision applications. SRAM based Field Programmable Gate Arrays (SRAM-FPGAs) are popular for the acceleration of CNNs. Since SRAM-FPGAs are prone to soft errors, the reliability evaluation and efficient fault tolerance design become very important for the use of FPGA-based CNNs in safety critical scenarios. Hardware based fault injection is an effective approach for the reliability evaluation, and the results can provide valuable references for the fault tolerance design. However, the complexity of building a fault injection platform poses a big obstacle for researchers working on the fault tolerance design. To remove this obstacle, this paper first performs a complete reliability evaluation for errors on the configuration memory of the FPGA based CNN accelerators, and then studies the impact of errors on the output feature maps of each layer. Based on the statistical analysis, we propose several fault models for the effect of SEUs on the configuration memory of the FPGA based CNN accelerators, and build a software simulator based on the fault models. Experiments show that the evaluation results based on the software simulator are very close to those from the hardware fault injections. Therefore, the proposed fault models and simulator can facilitate the fault tolerance design and reliability evaluation of CNN accelerators.