DA-FIS: A high-speed dynamic adaptive fault injection server framework for reliable FPGA-based embedded systems

Fault injection is a critical technique for assessing the reliability of field programmable gate array (FPGA)-based embedded systems, particularly in radiation-prone and safety-critical applications. Conventional fault injection methods, such as bit upset fault injection testing (BUFIT), single critical fault injection testing (SCFIT), and dynamic partial reconfiguration (DPR), suffer from high resource overhead, slow injection speeds, and limited adaptability, making them inadequate for real-time fault resilience evaluation. This article introduces the dynamic adaptive fault injection server (DA-FIS), a high-speed, scalable, and resource-efficient fault injection framework designed to overcome these limitations. Unlike traditional methods, DA-FIS employs a configurable LFSR-based fault generator that enables adaptive and real-time fault injection based on workload sensitivity and system conditions. The proposed framework integrates masking logic and dynamic propagation tracking, allowing precise injection of single-event upsets (SEUs) and multiple-bit upsets (MBUs) into FPGA configuration memory and logic without disturbing non-targeted regions. DA-FIS is implemented on the Xilinx Zynq-7000 FPGA and evaluated across multiple benchmark workloads, including the Bubble Sort algorithm, 4-bit adder, 4-bit multiplier, and counter-based logic circuits. Experimental results demonstrate that DA-FIS achieves a fault injection rate of 111.1 faults per second, outperforming BUFIT (53.4 faults/s), SCFIT (27 faults/s), and DPR (18.5 faults/s), with 30% lower FPGA resource overhead compared to SCFIT. The adaptive architecture ensures seamless scalability across different FPGA platforms, making it suitable for space electronics, automotive safety systems, and high-performance computing. Additionally, DA-FIS supports real-time error model adjustments, enabling researchers to analyze fault propagation, error correction strategies, and security vulnerabilities in FPGA-based architectures. This work establishes DA-FIS as a superior fault injection framework, offering high-speed, precision-controlled fault testing while maintaining minimal FPGA overhead and enhanced scalability. Future research will explore machine learning-assisted fault modeling and self-healing FPGA architectures to further enhance FPGA fault resilience in safety-critical and autonomous systems.