FPGA-based Inference Parallelization for Onboard RL-based Routing in Dynamic LEO Satellite Networks
This paper addresses the problem of onboard computer application of dynamic low-orbit satellite network routing algorithms. In low-orbit satellite networks, the satellite topology changes in real time, and satellite disconnection occurs frequently. The problem of routing algorithms for low-orbit sat...
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| Published in | International journal of aeronautical and space sciences Vol. 25; no. 3; pp. 1135 - 1145 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Seoul
The Korean Society for Aeronautical & Space Sciences (KSAS)
01.07.2024
한국항공우주학회 |
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| Abstract | This paper addresses the problem of onboard computer application of dynamic low-orbit satellite network routing algorithms. In low-orbit satellite networks, the satellite topology changes in real time, and satellite disconnection occurs frequently. The problem of routing algorithms for low-orbit satellites can be solved by reinforcement learning algorithms. However, the inference process based on deep reinforcement learning models suffers from excessive computation due to the operation of multiple convolutional layers. In this paper, we propose a method to accelerate convolutional layer operations by parallelizing them using heterogeneous processors. This approach is compared to the traditional single-processor-based convolutional operation method, commonly used in dynamic low-orbit satellite network routing algorithms. Our evaluation, conducted on an actual heterogeneous processor-based onboard computer, demonstrates that the proposed method not only matches the accuracy of the conventional single-processor-based approach, but also significantly reduces the execution time. |
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| AbstractList | This paper addresses the problem of onboard computer application of dynamic low-orbit satellite network routing algorithms. In low-orbit satellite networks, the satellite topology changes in real time, and satellite disconnection occurs frequently. The problem of routing algorithms for low-orbit satellites can be solved by reinforcement learning algorithms. However, the inference process based on deep reinforcement learning models suffers from excessive computation due to the operation of multiple convolutional layers. In this paper, we propose a method to accelerate convolutional layer operations by parallelizing them using heterogeneous processors. This approach is compared to the traditional single-processor-based convolutional operation method, commonly used in dynamic low-orbit satellite network routing algorithms. Our evaluation, conducted on an actual heterogeneous processor-based onboard computer, demonstrates that the proposed method not only matches the accuracy of the conventional single-processor-based approach, but also significantly reduces the execution time. KCI Citation Count: 0 This paper addresses the problem of onboard computer application of dynamic low-orbit satellite network routing algorithms. In low-orbit satellite networks, the satellite topology changes in real time, and satellite disconnection occurs frequently. The problem of routing algorithms for low-orbit satellites can be solved by reinforcement learning algorithms. However, the inference process based on deep reinforcement learning models suffers from excessive computation due to the operation of multiple convolutional layers. In this paper, we propose a method to accelerate convolutional layer operations by parallelizing them using heterogeneous processors. This approach is compared to the traditional single-processor-based convolutional operation method, commonly used in dynamic low-orbit satellite network routing algorithms. Our evaluation, conducted on an actual heterogeneous processor-based onboard computer, demonstrates that the proposed method not only matches the accuracy of the conventional single-processor-based approach, but also significantly reduces the execution time. |
| Author | Han, Myounghun Kim, Dohyung Lee, Heoncheol Won, Dongshik |
| Author_xml | – sequence: 1 givenname: Dohyung surname: Kim fullname: Kim, Dohyung organization: School of Electronic Engineering, Kumoh National Institute of Technology – sequence: 2 givenname: Heoncheol orcidid: 0000-0003-2962-3474 surname: Lee fullname: Lee, Heoncheol email: hclee@kumoh.ac.kr organization: Department of IT Convergence Engineering, School of Electronic Engineering, Kumoh National Institute of Technology – sequence: 3 givenname: Dongshik surname: Won fullname: Won, Dongshik organization: Future Innovation Research Team, TelePIX Corporation – sequence: 4 givenname: Myounghun surname: Han fullname: Han, Myounghun organization: Agency for Defense Development |
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| Copyright | The Author(s), under exclusive licence to The Korean Society for Aeronautical & Space Sciences 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| References | Herath HM (2021) Starlink: a solution to the digital connectivity divide in education in the global South. arXiv preprint arXiv:2110.09225 WangJTongWZhiXModel parallelism optimization for CNN FPGA acceleratorAlgorithms202316211010.3390/a16020110 ChenRWangWZhaoXZhaoGWaypoint segment routing algorithm for LEO satellite networkIET Commun202216182133214410.1049/cmu2.12466 Roh B, Han M, Kum D, Jeong K (2022) A Study on the Reinforcement Learning Routing for LEO Satellite Network.In: Korean Institute of Communication Sciences Conference, pp 537–538 Wang X, Dai Z, Xu Z (2021) LEO satellite network routing algorithm based on reinforcement learning.In: 2021 IEEE 4th International Conference on Electronics Technology(ICET), pp 1105–1109. https://doi.org/10.1109/ICET51757.2021.9451072 KimDHanYLeeHKimYKwonHKimCChoiWAccelerated particle filter with GPU for real-time ballistic target trackingIEEE Access2023112023121391214910.1109/ACCESS.2023.3238873 Zuo P, Wand C, Yao Z, Hou S, Jiang (2021) An intelligent routing algorithm for leo satellites based on deep reinforcement learning.In: 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), pp 1–5. https://doi.org/10.1109/vtc2021-fall52928.2021.9625325 HuandYShufanWZeyuKZhongchengMHuangHXiaofengWChengXReinforcement learning based dynamic distributed routing scheme for mega LEO satellite networksChin J Aeronaut20233628429110.1016/j.cja.2022.06.021 Qiu H, Liu F (2020) A state representation dueling network for deep reinforcement learning. In: 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), pp 669–674. https://doi.org/10.1109/ictai50040.2020.00107 LeeSLeeHKimYKimJChoiWGPU-accelerated PD-IPM for real-time model predictive control in integrated missile guidance and control systemsSensors20222212451210.3390/s22124512 KimDJungHPerformance analysis of target tracking AI based on unity ML-agentsJ JKIIT20211912192610.14801/jkiit.2021.19.12.19 Wang F, Agrawal V (2008) Single event upset:An embedded tutorial. In: 21st International Conference on VLSI Design (VLSID 2008), pp 429–439. https://doi.org/10.1109/vlsi.2008.28 Van Otterlo M (2009) Markov decision processes: Concepts and algorithms. Course on ‘Learning and Reasoning Lee J, Ko Y (2021) A Study on the low-earth orbit satellite based non-terrestrial network systems via deep-reinforcement learning. In: Korean Institute of Communication Sciences Conference, pp 1306-1307 Izhikevich L, Tran M, Izhikevich K, Akiwate G, Durumeric Z (2023) Democratizing LEO Satellite Network Measurement. arXiv preprint arXiv:2306.07469 HanYLeeHKwonHChoiWJeongBParallelized particle swarm optimization with GPU for real-time ballistic target trackingIEMEK J Embedded Syst Appl202217635536510.14372/IEMEK.2022.17.6.355 Booshehri M, Malekpour A, Luksch P (2013) An improving method for loop unrolling. arXiv preprint arXiv:1308.0698 Wang Z, Schaul T, Hessel M, Hasselt H, Lanctot M, Freitas N (2016) Dueling network architectures for deep reinforcement learning. In: International conference on machine learning. PMLR, 48, pp 1995-2003 Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M (2013) Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 de FineLJBestaMMeierhansSHoeflerTTransformations of high-level synthesis codes for high-performance computingIEEE Trans Parallel Distrib Syst20203251014102910.1109/tpds.2020.3039409 Lee Y, Kim K (2021) Cross-Point Based Routing Protocol in Low Earth Orbit Communication Networks.In: Proceedings of the Korea Information Processing Society Conference, 28(2), pp 72–74 Hutson J, Pellish J, Tipton A, Xaposos M, Xapsos M, Friendlich M, Campola M, Seidleck S, LaBel K, Marshall A, Deng X (2008) Analysis of Single Event Latchup Cross Section in 65nm SRAMs. In: IEEE Nuclear and Space Radiation Effects Conference (NSREC) BanTAn autonomous transmission scheme using dueling DQN for D2D communication networksIEEE Trans Veh Technol20206912163481635210.1109/tvt.2020.3041458 Vohra M, Fasciani S (2019) PYNQ-Torch: a framework to develop PyTorch accelerators on the PYNQ platform. In: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp 1–6. https://doi.org/10.1109/isspit47144.2019.9001806 Zuo P, Wang C, Wei Z, Li Z, Zhao H, Jiang H (2022) Deep Reinforcement Learning Based Load Balancing Routing for LEO Satellite Network. In: 2022 IEEE 95th Vehicular Technology Conference(VTC2022-Spring), pp 1–6. https://doi.org/10.1109/vtc2022-spring54318.2022.9860582 720_CR2 720_CR1 720_CR12 720_CR11 T Ban (720_CR20) 2020; 69 720_CR3 720_CR10 720_CR6 720_CR17 720_CR5 720_CR8 720_CR7 J Wang (720_CR13) 2023; 16 S Lee (720_CR15) 2022; 22 LJ de Fine (720_CR25) 2020; 32 720_CR24 720_CR23 D Kim (720_CR14) 2023; 11 D Kim (720_CR22) 2021; 19 Y Han (720_CR16) 2022; 17 720_CR9 R Chen (720_CR4) 2022; 16 720_CR19 720_CR18 Y Huand (720_CR21) 2023; 36 |
| References_xml | – reference: Roh B, Han M, Kum D, Jeong K (2022) A Study on the Reinforcement Learning Routing for LEO Satellite Network.In: Korean Institute of Communication Sciences Conference, pp 537–538 – reference: KimDJungHPerformance analysis of target tracking AI based on unity ML-agentsJ JKIIT20211912192610.14801/jkiit.2021.19.12.19 – reference: WangJTongWZhiXModel parallelism optimization for CNN FPGA acceleratorAlgorithms202316211010.3390/a16020110 – reference: Booshehri M, Malekpour A, Luksch P (2013) An improving method for loop unrolling. arXiv preprint arXiv:1308.0698 – reference: Van Otterlo M (2009) Markov decision processes: Concepts and algorithms. Course on ‘Learning and Reasoning – reference: ChenRWangWZhaoXZhaoGWaypoint segment routing algorithm for LEO satellite networkIET Commun202216182133214410.1049/cmu2.12466 – reference: LeeSLeeHKimYKimJChoiWGPU-accelerated PD-IPM for real-time model predictive control in integrated missile guidance and control systemsSensors20222212451210.3390/s22124512 – reference: HanYLeeHKwonHChoiWJeongBParallelized particle swarm optimization with GPU for real-time ballistic target trackingIEMEK J Embedded Syst Appl202217635536510.14372/IEMEK.2022.17.6.355 – reference: Lee Y, Kim K (2021) Cross-Point Based Routing Protocol in Low Earth Orbit Communication Networks.In: Proceedings of the Korea Information Processing Society Conference, 28(2), pp 72–74 – reference: de FineLJBestaMMeierhansSHoeflerTTransformations of high-level synthesis codes for high-performance computingIEEE Trans Parallel Distrib Syst20203251014102910.1109/tpds.2020.3039409 – reference: Izhikevich L, Tran M, Izhikevich K, Akiwate G, Durumeric Z (2023) Democratizing LEO Satellite Network Measurement. arXiv preprint arXiv:2306.07469 – reference: Wang Z, Schaul T, Hessel M, Hasselt H, Lanctot M, Freitas N (2016) Dueling network architectures for deep reinforcement learning. In: International conference on machine learning. PMLR, 48, pp 1995-2003 – reference: Wang F, Agrawal V (2008) Single event upset:An embedded tutorial. In: 21st International Conference on VLSI Design (VLSID 2008), pp 429–439. https://doi.org/10.1109/vlsi.2008.28 – reference: Zuo P, Wand C, Yao Z, Hou S, Jiang (2021) An intelligent routing algorithm for leo satellites based on deep reinforcement learning.In: 2021 IEEE 94th Vehicular Technology Conference (VTC2021-Fall), pp 1–5. https://doi.org/10.1109/vtc2021-fall52928.2021.9625325 – reference: Vohra M, Fasciani S (2019) PYNQ-Torch: a framework to develop PyTorch accelerators on the PYNQ platform. In: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp 1–6. https://doi.org/10.1109/isspit47144.2019.9001806 – reference: Herath HM (2021) Starlink: a solution to the digital connectivity divide in education in the global South. arXiv preprint arXiv:2110.09225 – reference: KimDHanYLeeHKimYKwonHKimCChoiWAccelerated particle filter with GPU for real-time ballistic target trackingIEEE Access2023112023121391214910.1109/ACCESS.2023.3238873 – reference: HuandYShufanWZeyuKZhongchengMHuangHXiaofengWChengXReinforcement learning based dynamic distributed routing scheme for mega LEO satellite networksChin J Aeronaut20233628429110.1016/j.cja.2022.06.021 – reference: Lee J, Ko Y (2021) A Study on the low-earth orbit satellite based non-terrestrial network systems via deep-reinforcement learning. In: Korean Institute of Communication Sciences Conference, pp 1306-1307 – reference: BanTAn autonomous transmission scheme using dueling DQN for D2D communication networksIEEE Trans Veh Technol20206912163481635210.1109/tvt.2020.3041458 – reference: Qiu H, Liu F (2020) A state representation dueling network for deep reinforcement learning. In: 2020 IEEE 32nd International Conference on Tools with Artificial Intelligence (ICTAI), pp 669–674. https://doi.org/10.1109/ictai50040.2020.00107 – reference: Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M (2013) Playing Atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602 – reference: Hutson J, Pellish J, Tipton A, Xaposos M, Xapsos M, Friendlich M, Campola M, Seidleck S, LaBel K, Marshall A, Deng X (2008) Analysis of Single Event Latchup Cross Section in 65nm SRAMs. In: IEEE Nuclear and Space Radiation Effects Conference (NSREC) – reference: Zuo P, Wang C, Wei Z, Li Z, Zhao H, Jiang H (2022) Deep Reinforcement Learning Based Load Balancing Routing for LEO Satellite Network. In: 2022 IEEE 95th Vehicular Technology Conference(VTC2022-Spring), pp 1–6. https://doi.org/10.1109/vtc2022-spring54318.2022.9860582 – reference: Wang X, Dai Z, Xu Z (2021) LEO satellite network routing algorithm based on reinforcement learning.In: 2021 IEEE 4th International Conference on Electronics Technology(ICET), pp 1105–1109. https://doi.org/10.1109/ICET51757.2021.9451072 – volume: 17 start-page: 355 issue: 6 year: 2022 ident: 720_CR16 publication-title: IEMEK J Embedded Syst Appl doi: 10.14372/IEMEK.2022.17.6.355 – volume: 36 start-page: 284 year: 2023 ident: 720_CR21 publication-title: Chin J Aeronaut doi: 10.1016/j.cja.2022.06.021 – ident: 720_CR17 – ident: 720_CR19 doi: 10.1109/ictai50040.2020.00107 – volume: 69 start-page: 16348 issue: 12 year: 2020 ident: 720_CR20 publication-title: IEEE Trans Veh Technol doi: 10.1109/tvt.2020.3041458 – volume: 32 start-page: 1014 issue: 5 year: 2020 ident: 720_CR25 publication-title: IEEE Trans Parallel Distrib Syst doi: 10.1109/tpds.2020.3039409 – volume: 19 start-page: 19 issue: 12 year: 2021 ident: 720_CR22 publication-title: J JKIIT doi: 10.14801/jkiit.2021.19.12.19 – ident: 720_CR11 doi: 10.1109/vlsi.2008.28 – ident: 720_CR6 doi: 10.1109/ICET51757.2021.9451072 – volume: 16 start-page: 110 issue: 2 year: 2023 ident: 720_CR13 publication-title: Algorithms doi: 10.3390/a16020110 – ident: 720_CR2 doi: 10.1145/3652963.3655052 – ident: 720_CR24 – volume: 22 start-page: 4512 issue: 12 year: 2022 ident: 720_CR15 publication-title: Sensors doi: 10.3390/s22124512 – ident: 720_CR18 – ident: 720_CR7 doi: 10.1109/vtc2021-fall52928.2021.9625325 – ident: 720_CR8 doi: 10.1109/vtc2022-spring54318.2022.9860582 – ident: 720_CR9 – ident: 720_CR12 – volume: 11 start-page: 12139 issue: 2023 year: 2023 ident: 720_CR14 publication-title: IEEE Access doi: 10.1109/ACCESS.2023.3238873 – ident: 720_CR10 – volume: 16 start-page: 2133 issue: 18 year: 2022 ident: 720_CR4 publication-title: IET Commun doi: 10.1049/cmu2.12466 – ident: 720_CR23 doi: 10.1109/isspit47144.2019.9001806 – ident: 720_CR3 – ident: 720_CR5 – ident: 720_CR1 |
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| Title | FPGA-based Inference Parallelization for Onboard RL-based Routing in Dynamic LEO Satellite Networks |
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