Soft error susceptibility analysis methodology of HLS designs in SRAM-based FPGAs

Abstract SRAM-based FPGAs are attractive to critical applications due to their reconfiguration capability, which allows the design to be adapted on the field under different upset rate environments. High level Synthesis (HLS) is a powerful method to explore different design architectures in FPGAs. In this paper, the HLS tool from Xilinx is used to generate different design architectures and then analyze the probability of errors in those architectures. Two different case studies scenarios are investigated. First, it is evaluated the influence of control flow and pipeline architectures combined with the use of specialized DSP blocks in the FPGA. The number of errors classified as silent data corruption and timeout according to the architectures and DSP blocks usage is analyzed. Moreover, more possibilities of HLS designs are explored such as data organization, aggressive pipeline insertion and the implementation of the algorithm in a soft processor like the Microblaze from Xilinx. These architectures are strongly optimized in performance and the least susceptible design under soft errors is investigated. All case-study designs are evaluated in a 28 nm SRAM-based FPGA under fault injection. The dynamic cross section, soft error rate and mean work between failures are calculated based on the experimental results. The proposed characterization method can be used to guide designers to select better architectures concerning the susceptibility to upsets and performance efficiency.