Evaluation of SEU impact on convolutional neural networks based on BRAM and CRAM in FPGAs. (May 2023) - Tian, Haonan; Ibrahim, Younis; Chen, Rui; Jin, Chen; Shi, Shuting; Xing, Jiesi; Li, Jianjun; Chen, Li
Abstract: This study evaluates the sensitivity of FPGA-based CNN systems and the efficacy of selective hardening approaches with laser injection and proton irradiation. The LeNet-5 CNN architecture was taken as a case study. First, the entire CNN with block random access memory (BRAM) attached is evaluated by laser test scanning, and errors generated from each layer are detected to determine the most critical layer. Second, the same network is evaluated without BRAM, considering only the configuration RAM (CRAM) and errors generated from each layer are detected. Third, the impact of laser scan errors from BRAM and CRAM are compared, while tracing the layers in which these errors are generated. Then, proton testing is performed on the entire CNN, and errors from each layer are detected and compared to validate the laser scanning test. Experimental results from laser and proton demonstrate that CRAM errors have a larger impact on CNN layers than BRAM errors. Regarding layer criticality, the second convolutional layer (C2) appears to be the most critical one because it generates most CRAM errors. This was validated by serial and parallel designs, where both confirm similar trends. Accordingly, the partial triple modular redundancy (TMR) approach is only applied to the critical portions, leading to around 40 % reliability improvement with less than 20 % overhead. Highlights: Evaluates the sensitivity of FPGA-based Convolutional neural networks with laser injection and proton irradiation The impact of BRAM and CRAM within CNN layers Layer criticality identification Carry out radiation hardened by design approach