Machine learning based adaptive soft error mitigation efficiency

This work presents a novel adaptive framework for soft error mitigation in space-based systems, designed to resolve the fundamental conflict between system performance and radiation protection. By leveraging a Long Short-Term Memory (LSTM) model to predict real-time solar particle flux, our approach dynamically enables or disables software-based mitigation techniques. This contrasts with the static, "always-on" methods of existing systems, offering a significant improvement in computational efficiency. The proposed LSTM model was trained on NASA solar particle flux data, achieving a mean average error of 7.65e-6, demonstrating its high accuracy in predicting nonlinear particle events. Our simulation, which applies this predictive model to a tiered system of redundant processing, checkpointing, and watchdog timers, shows a substantial reduction in overhead. During the 18,414-second test period, the combined adaptive mitigation methods introduced only 20.75–51.6 seconds of overhead, representing a 99.4% reduction in overhead compared to continuous, static mitigation. This research's primary contribution is a demonstrated proof-of-concept for an intelligent, self-adaptive system that can maintain high reliability while drastically improving performance. This approach provides a pathway for utilizing more cost-effective commercial-off-the-shelf (COTS) processors in radiation-intensive environments.