Hardware Accelerator Design with Supervised Machine Learning for Solar Particle Event Prediction

Abstract

The intensity of cosmic radiation can differ over five orders of magnitude within a few hours or days during Solar Particle Events (SPEs), thus increasing the probability of Single-Event Upsets (SEUs) in space applications for several orders of magnitude. Therefore, it is vital to employ the early detection of the SEU rate changes in order to ensure timely activation of the radiation hardening measures. In this paper, a hardware accelerator for forecasting the SPEs by the prediction of in-flight SEU variation is proposed. An embedded on-chip SRAM is used as the real-time particle detector. The dedicated hardware accelerator implements a supervised machine learning model to forecast the SRAM SEUs one hour in advance with fine-grained hourly tracking of SEU variations during SPEs as well as under normal conditions. The whole design is intended for a highly dependable and self-adaptive multiprocessing system employed in space applications. Therefore, the target system can drive the appropriate radiation hardening mechanisms before the onset of high radiation levels.