BAFFI: a bit-accurate fault injector for improved dependability assessment of FPGA prototypes

Handle

https://riunet.upv.es/handle/10251/202094

Cita bibliográfica

Tuzov, I.; De-Andrés-Martínez, D.; Ruiz, JC.; Hernández Luz, C. (2023). BAFFI: a bit-accurate fault injector for improved dependability assessment of FPGA prototypes. IEEE. https://doi.org/10.23919/DATE56975.2023.10137300

Titulación

Resumen

[EN] FPGA-based fault injection (FFI) is an indispensable technique for verification and dependability assessment of FPGA designs and prototypes. Existing FFI tools make use of Xilinx essential bits technology to locate the relevant fault targets in FPGA configuration memory (CM). Most FFI tools treat essential bits as black-box, while few of them are able to filter essential bits on the area basis in order to selectively target design components contained within the predefined Pblocks. This approach, however, remains insufficiently precise since the granularity of Pblocks in practice does not reach the smallest design components. This paper proposes an open-source FFI tool that enables much more fine-grained FFI experiments for Xilinx 7-series and Ultrascale+ FPGAs. By mapping the essential bits with the hierarchical netlist, it allows to precisely target any component in the design tree, up to an individual LUT or register, without the need for defining Pblocks (floorplanning). With minimal experimental effort it estimates the contribution of each DUT component into the resulting dependability features, and discovers weak points of the DUT. Through case studies we show how the proposed tool can be applied to different kinds of DUTs: from small-footprint microcontrollers, up to multicore RISC-V SoC. The correctness of FFI results is validated by means of RT-level and gate-level simulation-based fault injection.

Palabras clave

Fault injection, FPGA, Configuration memory, Robustness assessment, RISC-V

ISSN

1938-1891

ISBN

978-3-9819263-7-8

Fuente

2023 Design, Automation & Test in Europe Conference & Exhibition (DATE)

DOI

10.23919/DATE56975.2023.10137300

Editorial

IEEE