Nov
21
Paper accepted at DATE 2023
November 21, 2022 | | Comments Off on Paper accepted at DATE 2023
The paper entitled “BAFFI: a bit-accurate fault injector for improved dependability assessment of FPGA prototypes”, authored by Ilya Tuzov, David de Andres, Juan-Carlos Ruiz and Carles Hernandez has been accepted at DATE 2023.
Abstract
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 (grid coordinates) 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 (netlist) 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 exploited to setup FFI experiments for 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.
Keywords: Fault injection, FPGA, configuration memory, robustness assessment, RISC-V
DEFADAS Project: Grant PID2020-120271RB-I00 funded by MCIN/AEI/10.13039/501100011033
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