Refine
Has Fulltext
- no (2)
Language
- English (2)
Is part of the Bibliography
- yes (2)
Keywords
Institute
- Institut für Physik und Astronomie (2) (remove)
This work introduces an embedded approach for the prediction of Solar Particle Events (SPEs) in space applications by combining the real-time Soft Error Rate (SER) measurement with SRAM-based detector and the offline trained machine learning model. The proposed approach is intended for the self-adaptive fault-tolerant multiprocessing systems employed in space applications. With respect to the state-of-the-art, our solution allows for predicting the SER 1 h in advance and fine-grained hourly tracking of SER variations during SPEs as well as under normal conditions. Therefore, the target system can activate the appropriate mechanisms for radiation hardening before the onset of high radiation levels. Based on the comparison of five different machine learning algorithms trained with the public space flux database, the preliminary results indicate that the best prediction accuracy is achieved with the recurrent neural network (RNN) with long short-term memory (LSTM).
A hybrid design approach of the hierarchical physical implementation design flow is presented and demonstrated on a fault-tolerant low-power multiprocessor system. The proposed flow allows to implement selected submodules in parallel with contrary requirements such as identical placement and individual block implementation. The overall system contains four Leon2 cores and communicates via the Waterbear framework and supports Adaptive Voltage Scaling (AVS) functionality. Three of the processor core variants are derived from the first baseline reference core but implemented individually at block level based on their clock tree specification. The chip is prepared for space applications and designed with triple modular redundancy (TMR) for control parts. The low-power performance is enabled by contemporary power and clock management control. An ASIC is fabricated in a low-power 0.13 mu m BiCMOS technology process node.