Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design
To meet the high-reliability requirements of real-time on-orbit tasks, this paper proposes a fault-tolerant reinforcement design method for spaceborne intelligent processing algorithms based on convolutional neural networks (CNNs). This method is built on a CNN accelerator using Field-Programmable G...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/1/69 |
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| author | Yingzhao Shao Junyi Wang Xiaodong Han Yunsong Li Yaolin Li Zhanpeng Tao |
| author_facet | Yingzhao Shao Junyi Wang Xiaodong Han Yunsong Li Yaolin Li Zhanpeng Tao |
| author_sort | Yingzhao Shao |
| collection | DOAJ |
| description | To meet the high-reliability requirements of real-time on-orbit tasks, this paper proposes a fault-tolerant reinforcement design method for spaceborne intelligent processing algorithms based on convolutional neural networks (CNNs). This method is built on a CNN accelerator using Field-Programmable Gate Array (FPGA) technology, analyzing the impact of Single-Event Upsets (SEUs) on neural network computation. The accelerator design integrates data validation, Triple Modular Redundancy (TMR), and other techniques, optimizing a partial fault-tolerant architecture based on SEU sensitivity. This fault-tolerant architecture analyzes the hardware accelerator, parameter storage, and actual computation, employing data validation to reinforce model parameters and spatial and temporal TMR to reinforce accelerator computations. Using the ResNet18 model, fault tolerance performance tests were conducted by simulating SEUs. Compared to the prototype network, this fault-tolerant design method increases tolerance to SEU error accumulation by five times while increasing resource consumption by less than 15%, making it more suitable for spaceborne on-orbit applications than traditional fault-tolerant design approaches. |
| format | Article |
| id | doaj-art-5c75605a47374ac7aefead3d5d033bca |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-5c75605a47374ac7aefead3d5d033bca2025-08-20T02:27:39ZengMDPI AGRemote Sensing2072-42922024-12-011716910.3390/rs17010069Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance DesignYingzhao Shao0Junyi Wang1Xiaodong Han2Yunsong Li3Yaolin Li4Zhanpeng Tao5State Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaChina Academy of Space Technology (Xi’an), Xi’an 710100, ChinaState Key Laboratory of Integrated Services Networks, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaSchool of Computer Science and Technology, Xidian University, Xi’an 710071, ChinaTo meet the high-reliability requirements of real-time on-orbit tasks, this paper proposes a fault-tolerant reinforcement design method for spaceborne intelligent processing algorithms based on convolutional neural networks (CNNs). This method is built on a CNN accelerator using Field-Programmable Gate Array (FPGA) technology, analyzing the impact of Single-Event Upsets (SEUs) on neural network computation. The accelerator design integrates data validation, Triple Modular Redundancy (TMR), and other techniques, optimizing a partial fault-tolerant architecture based on SEU sensitivity. This fault-tolerant architecture analyzes the hardware accelerator, parameter storage, and actual computation, employing data validation to reinforce model parameters and spatial and temporal TMR to reinforce accelerator computations. Using the ResNet18 model, fault tolerance performance tests were conducted by simulating SEUs. Compared to the prototype network, this fault-tolerant design method increases tolerance to SEU error accumulation by five times while increasing resource consumption by less than 15%, making it more suitable for spaceborne on-orbit applications than traditional fault-tolerant design approaches.https://www.mdpi.com/2072-4292/17/1/69spaceborne computingsingle-event upsetconvolutional neural networkfield-programmable gate arrayfault-tolerant design |
| spellingShingle | Yingzhao Shao Junyi Wang Xiaodong Han Yunsong Li Yaolin Li Zhanpeng Tao Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design Remote Sensing spaceborne computing single-event upset convolutional neural network field-programmable gate array fault-tolerant design |
| title | Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design |
| title_full | Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design |
| title_fullStr | Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design |
| title_full_unstemmed | Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design |
| title_short | Research on Spaceborne Neural Network Accelerator and Its Fault Tolerance Design |
| title_sort | research on spaceborne neural network accelerator and its fault tolerance design |
| topic | spaceborne computing single-event upset convolutional neural network field-programmable gate array fault-tolerant design |
| url | https://www.mdpi.com/2072-4292/17/1/69 |
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