2026-06-27T16:05:47Zhttps://docta.ucm.es/rest/oai/request
oai:docta.ucm.es:20.500.14352/63102023-08-26T13:16:59Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense author Ramezani, Reza author Clemente Barreira, Juan Antonio author Franco Peláez, Francisco Javier 2023-06-16T15:18:51Z 2023-06-16T15:18:51Z 2020-05-20 0951-8320 10.1016/j.ress.2020.107036 https://hdl.handle.net/20.500.14352/6310 https://doi.org/10.1016/j.ress.2020.107036 This paper addresses the problem of hardware tasks reliability estimation in harsh environments. A novel statistical model is presented to estimate the reliability, the mean time to failure, and the number of errors of hardware tasks running on SRAM-based partially run-time reconfigurable FPGAs in harsh environments by taking both single-bit upsets and multiple-cell upsets into account. The model requires some features of the hardware tasks, including their computation time, size, the percent of critical bits, and the soft error rates of k-bit events (k ≥ 1) of the environment for the reliability estimation. Such an early estimation helps the developers to assess the reliability of their designs at earlier stages and leads to reduce the development cost. The proposed model has been evaluated by conducting several experiments on actual hardware tasks over different environmental soft error rates. The obtained results, endorsed by the 95% confidence interval, reveal the high accuracy of the proposed model. When comparing this approach with a reliability model (developed by the authors in a previous work) that does not consider the occurrence of multiple-cell upsets, an overestimation of the mean time to failure of 2.88X is observable in the latter. This points to the importance of taking into account multiple events, especially in modern technologies where the miniaturization is high. eng Analytical Reliability Estimation of SRAM-based FPGA Designs against Single-bit and Multiple-cell Upsets journal article URL https://docta.ucm.es/bitstreams/fe4d24c5-8575-425e-aad3-3fce7989e98c/download File MD5 0e014221df2290c1fdc21305b6cd34c0 622587 application/pdf Paper 19.pdf URL https://docta.ucm.es/bitstreams/2bc36431-7630-4f65-a0ce-12eaa47e43ec/download File MD5 c4be686187bb95d312f3256de5bfe20e 52901 text/plain Paper 19.pdf.txt