Intelligent Simulation Platform for Fault Injection to Configuration Bitstream Based on Multiple Cores Architecture Processor
2024 20th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)(2024)
China Academy of Space Technology(Xi'an) | School of Aerospace Science and Technology
Abstract
The large scale digital circuit in space radiation is susceptible to single event upset caused by single event effect. It is particularly important to reinforce the FPGA from the perspective of reliability. The cost of conducting real irradiation experiments is too high and too expensive. Therefore, this paper proposes a SEM IP core based method to design an experimental and verification platform for simulating single event upset. This method performs bit-by-bit fault injection on the configuration bits in the user memory through SEM IP, and reads back to judge the consistency to verify the correctness of the method. The results show that the platform can inject faults accurately and quickly to meet the requirements of simulating space environment.
MoreTranslated text
Key words
Fault injection,Single event upset,Multiple cores processors,FPGA
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined