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

Junchao Chen, T. Lange, M. Andjelković, A. Simevski, M. Krstic
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引用次数: 3

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.
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基于监督机器学习的太阳粒子事件预测硬件加速器设计
在太阳粒子事件(spe)期间,宇宙辐射的强度在几小时或几天内可以变化超过五个数量级,从而在空间应用中增加了几个数量级的单事件扰动(SEUs)的可能性。因此,为了确保及时启动辐射硬化措施,早期检测SEU速率变化至关重要。本文提出了一种通过预测飞行中SEU变化来预测spe的硬件加速器。采用嵌入式片上SRAM作为实时粒子检测器。专用硬件加速器实现了监督机器学习模型,可以提前一小时预测SRAM SEU,并对spe期间和正常情况下的SEU变化进行细粒度的每小时跟踪。整个设计旨在为空间应用提供一个高度可靠和自适应的多处理系统。因此,目标系统可以在高辐射水平开始之前驱动适当的辐射硬化机制。
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