Late Breaking Results: Never-Stopping Inference: Self-Healing AI Accelerators on SRAM-FPGAs

This work presents a self-healing runtime for AI accelerators on SRAM-based FPGAs that combines online fault detection with fine-grained partial reconfiguration to ensure continuous inference execution. The framework dynamically isolates and repairs faulty regions while remapping workloads to healthy resources, eliminating the need for redundant hardware and system downtime. The proposed approach reduces recovery latency by 3 orders of magnitude compared to the state-of-the-art.