Fast SEU Detection and Recovery in FPGA-Based AI Accelerators

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Abstract
Heterogeneous FPGA platforms combining RISC-V processors and deep-learning accelerators are increasingly adopted in avionics and space, where performance must be paired with fault tolerance. We present RePAIR, a reconfigurable platform integrating an open-source RISC-V core with a TPU-like systolic-array accelerator for runtime fault detection, correction, and recovery. RePAIR extends the accelerator ISA with runtime self-test, enabling detection of structural faults in the array during inference. The platform supports dual inference modes: a plain mode with no overhead and a testing mode that performs checksum validation at a fixed cost of three extra cycles per matrix multiplication, with limited accelerator area overhead. Upon fault detection, the accelerator notifies the RISC-V processor, which triggers dynamic partial reconfiguration of the faulty region while preserving execution state, allowing inference to resume from the last correct step. Compared with full-device reconfiguration, recovery time is reduced by up to 900× on AMD KCU105 and 1400× on AMD ZCU102, while inference overhead remains ≤30% in the worst case. The methodology is hardware-agnostic and portable across FPGA devices, as shown by multi-platform implementations. Fault-injection campaigns combined with space-environment modeling estimate mean time to failure under mission conditions, demonstrating scalable and reliable FPGA-based AI acceleration for safety-critical applications.

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