Model and Method for Providing Resilience to Resource-Constrained AI-System
Artificial intelligence technologies are becoming increasingly prevalent in resource-constrained, safety-critical embedded systems. Numerous methods exist to enhance the resilience of AI systems against disruptive influences. However, when resources are limited, ensuring cost-effective resilience be...
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| Vydáno v: | Sensors (Basel, Switzerland) Ročník 24; číslo 18; s. 5951 |
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13.09.2024
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| Abstract | Artificial intelligence technologies are becoming increasingly prevalent in resource-constrained, safety-critical embedded systems. Numerous methods exist to enhance the resilience of AI systems against disruptive influences. However, when resources are limited, ensuring cost-effective resilience becomes crucial. A promising approach for reducing the resource consumption of AI systems during test-time involves applying the concepts and methods of dynamic neural networks. Nevertheless, the resilience of dynamic neural networks against various disturbances remains underexplored. This paper proposes a model architecture and training method that integrate dynamic neural networks with a focus on resilience. Compared to conventional training methods, the proposed approach yields a 24% increase in the resilience of convolutional networks and a 19.7% increase in the resilience of visual transformers under fault injections. Additionally, it results in a 16.9% increase in the resilience of convolutional network ResNet-110 and a 21.6% increase in the resilience of visual transformer DeiT-S under adversarial attacks, while saving more than 30% of computational resources. Meta-training the neural network model improves resilience to task changes by an average of 22%, while achieving the same level of resource savings. |
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| AbstractList | Artificial intelligence technologies are becoming increasingly prevalent in resource-constrained, safety-critical embedded systems. Numerous methods exist to enhance the resilience of AI systems against disruptive influences. However, when resources are limited, ensuring cost-effective resilience becomes crucial. A promising approach for reducing the resource consumption of AI systems during test-time involves applying the concepts and methods of dynamic neural networks. Nevertheless, the resilience of dynamic neural networks against various disturbances remains underexplored. This paper proposes a model architecture and training method that integrate dynamic neural networks with a focus on resilience. Compared to conventional training methods, the proposed approach yields a 24% increase in the resilience of convolutional networks and a 19.7% increase in the resilience of visual transformers under fault injections. Additionally, it results in a 16.9% increase in the resilience of convolutional network ResNet-110 and a 21.6% increase in the resilience of visual transformer DeiT-S under adversarial attacks, while saving more than 30% of computational resources. Meta-training the neural network model improves resilience to task changes by an average of 22%, while achieving the same level of resource savings. Artificial intelligence technologies are becoming increasingly prevalent in resource-constrained, safety-critical embedded systems. Numerous methods exist to enhance the resilience of AI systems against disruptive influences. However, when resources are limited, ensuring cost-effective resilience becomes crucial. A promising approach for reducing the resource consumption of AI systems during test-time involves applying the concepts and methods of dynamic neural networks. Nevertheless, the resilience of dynamic neural networks against various disturbances remains underexplored. This paper proposes a model architecture and training method that integrate dynamic neural networks with a focus on resilience. Compared to conventional training methods, the proposed approach yields a 24% increase in the resilience of convolutional networks and a 19.7% increase in the resilience of visual transformers under fault injections. Additionally, it results in a 16.9% increase in the resilience of convolutional network ResNet-110 and a 21.6% increase in the resilience of visual transformer DeiT-S under adversarial attacks, while saving more than 30% of computational resources. Meta-training the neural network model improves resilience to task changes by an average of 22%, while achieving the same level of resource savings.Artificial intelligence technologies are becoming increasingly prevalent in resource-constrained, safety-critical embedded systems. Numerous methods exist to enhance the resilience of AI systems against disruptive influences. However, when resources are limited, ensuring cost-effective resilience becomes crucial. A promising approach for reducing the resource consumption of AI systems during test-time involves applying the concepts and methods of dynamic neural networks. Nevertheless, the resilience of dynamic neural networks against various disturbances remains underexplored. This paper proposes a model architecture and training method that integrate dynamic neural networks with a focus on resilience. Compared to conventional training methods, the proposed approach yields a 24% increase in the resilience of convolutional networks and a 19.7% increase in the resilience of visual transformers under fault injections. Additionally, it results in a 16.9% increase in the resilience of convolutional network ResNet-110 and a 21.6% increase in the resilience of visual transformer DeiT-S under adversarial attacks, while saving more than 30% of computational resources. Meta-training the neural network model improves resilience to task changes by an average of 22%, while achieving the same level of resource savings. |
| Audience | Academic |
| Author | Semenov, Serhii Kharchenko, Vyacheslav Moskalenko, Viacheslav |
| AuthorAffiliation | 1 Department of Computer Science, Sumy State University, 116, Kharkivska Str., 40007 Sumy, Ukraine 2 Department of Computer Systems, Networks and Cybersecurity, National Aerospace University “KhAI”, 17, Chkalov Str., 61070 Kharkiv, Ukraine; v.kharchenko@csn.khai.edu 3 Cyber Security Department, University of the National Education Commission, Ul. Podchorążych 2, 30-084 Kraków, Poland; serhii.semenov@up.krakow.pl |
| AuthorAffiliation_xml | – name: 2 Department of Computer Systems, Networks and Cybersecurity, National Aerospace University “KhAI”, 17, Chkalov Str., 61070 Kharkiv, Ukraine; v.kharchenko@csn.khai.edu – name: 1 Department of Computer Science, Sumy State University, 116, Kharkivska Str., 40007 Sumy, Ukraine – name: 3 Cyber Security Department, University of the National Education Commission, Ul. Podchorążych 2, 30-084 Kraków, Poland; serhii.semenov@up.krakow.pl |
| Author_xml | – sequence: 1 givenname: Viacheslav orcidid: 0000-0001-6275-9803 surname: Moskalenko fullname: Moskalenko, Viacheslav – sequence: 2 givenname: Vyacheslav orcidid: 0000-0001-5352-077X surname: Kharchenko fullname: Kharchenko, Vyacheslav – sequence: 3 givenname: Serhii orcidid: 0000-0003-4472-9234 surname: Semenov fullname: Semenov, Serhii |
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| Cites_doi | 10.1109/IOLTS56730.2022.9897813 10.1109/ICPR48806.2021.9413182 10.1007/s00138-024-01519-1 10.1109/TNNLS.2021.3107051 10.1109/COMST.2020.3036778 10.1109/ISSRE5003.2020.00047 10.1016/j.neucom.2022.11.072 10.1109/ICASSP49357.2023.10096662 10.1109/SiPS47522.2019.9020551 10.1007/978-3-030-01246-5_1 10.1109/IPFA49335.2020.9261013 10.1038/s42256-023-00626-4 10.32620/reks.2022.3.07 10.1109/ICIP49359.2023.10222298 10.1109/ISSREW.2018.00024 10.20944/preprints202302.0209.v1 10.3389/fpubh.2024.1342937 10.1016/j.media.2021.102141 10.52591/lxai202307232 10.3390/computers12030060 10.1371/journal.pone.0265723 10.1109/ICCVW60793.2023.00163 10.1109/CVPR52688.2022.01199 10.3390/make5040080 10.1145/3659943 10.1109/IROS45743.2020.9341571 |
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| References | Moskalenko (ref_34) 2023; 2 ref_35 ref_12 ref_11 ref_33 ref_32 ref_31 Ding (ref_18) 2023; 5 ref_19 Eleftheriadis (ref_13) 2024; 35 ref_17 ref_16 ref_15 Lysenko (ref_36) 2020; 1 Hussain (ref_6) 2023; 5 ref_25 ref_24 ref_22 Bortsova (ref_30) 2021; 73 ref_21 ref_20 ref_1 ref_3 Sum (ref_14) 2023; 34 ref_29 Olowononi (ref_4) 2021; 23 ref_28 Moskalenko (ref_23) 2022; 3 ref_27 ref_26 Petrini (ref_2) 2023; 520 ref_9 ref_8 ref_5 ref_7 Guo (ref_10) 2024; 238 |
| References_xml | – ident: ref_7 – ident: ref_11 doi: 10.1109/IOLTS56730.2022.9897813 – ident: ref_5 – ident: ref_24 – ident: ref_33 doi: 10.1109/ICPR48806.2021.9413182 – volume: 35 start-page: 3 year: 2024 ident: ref_13 article-title: Adversarial robustness improvement for deep neural networks publication-title: Mach. Vis. Appl. doi: 10.1007/s00138-024-01519-1 – volume: 34 start-page: 2619 year: 2023 ident: ref_14 article-title: Regularization Effect of Random Node Fault/Noise on Gradient Descent Learning Algorithm publication-title: IEEE Trans. Neural Netw. Learn. Syst. doi: 10.1109/TNNLS.2021.3107051 – volume: 23 start-page: 524 year: 2021 ident: ref_4 article-title: Resilient Machine Learning for Networked Cyber Physical Systems: A Survey for Machine Learning Security to Securing Machine Learning for CPS publication-title: IEEE Commun. Surv. Tutor. doi: 10.1109/COMST.2020.3036778 – ident: ref_35 doi: 10.1109/ISSRE5003.2020.00047 – volume: 520 start-page: 152 year: 2023 ident: ref_2 article-title: Deep neural networks compression: A comparative survey and choice recommendations publication-title: Neurocomputing doi: 10.1016/j.neucom.2022.11.072 – ident: ref_25 doi: 10.1109/ICASSP49357.2023.10096662 – ident: ref_20 doi: 10.1109/SiPS47522.2019.9020551 – ident: ref_21 doi: 10.1007/978-3-030-01246-5_1 – volume: 2 start-page: 79 year: 2023 ident: ref_34 article-title: Model-Agnostic Meta-Learning for Resilience Optimization of Artificial Intelligence System. Radio Electron publication-title: Comput. Sci. Control – ident: ref_9 doi: 10.1109/IPFA49335.2020.9261013 – volume: 5 start-page: 220 year: 2023 ident: ref_18 article-title: Parameter-efficient fine-tuning of large-scale pre-trained language models publication-title: Nat. Mach. Intell. doi: 10.1038/s42256-023-00626-4 – volume: 3 start-page: 95 year: 2022 ident: ref_23 article-title: Neural network based image classifier resilient to destructive perturbation influences—Architecture and training method publication-title: Radioelectron. Comput. Syst. doi: 10.32620/reks.2022.3.07 – volume: 1 start-page: 17 year: 2020 ident: ref_36 article-title: Computer systems resilience in the presence of cyber threats: Taxonomy and ontology publication-title: Radioelectron. Comput. Syst. – ident: ref_27 doi: 10.1109/ICIP49359.2023.10222298 – ident: ref_32 doi: 10.1109/ISSREW.2018.00024 – ident: ref_3 doi: 10.20944/preprints202302.0209.v1 – ident: ref_28 doi: 10.3389/fpubh.2024.1342937 – ident: ref_12 – volume: 73 start-page: 102141 year: 2021 ident: ref_30 article-title: Adversarial attack vulnerability of medical image analysis systems: Unexplored factors publication-title: Med. Image Anal. doi: 10.1016/j.media.2021.102141 – ident: ref_8 doi: 10.52591/lxai202307232 – ident: ref_1 doi: 10.3390/computers12030060 – volume: 238 start-page: 4393 year: 2024 ident: ref_10 article-title: The effect of Leaky ReLUs on the training and generalization of overparameterized networks publication-title: Proc. Mach. Learn. Res. – ident: ref_15 – ident: ref_31 doi: 10.1371/journal.pone.0265723 – ident: ref_22 doi: 10.1109/ICCVW60793.2023.00163 – ident: ref_26 doi: 10.1109/CVPR52688.2022.01199 – ident: ref_17 – ident: ref_19 – volume: 5 start-page: 1589 year: 2023 ident: ref_6 article-title: Reconstruction-Based Adversarial Attack Detection in Vision-Based Autonomous Driving Systems publication-title: Mach. Learn. Knowl. Extr. doi: 10.3390/make5040080 – ident: ref_29 doi: 10.1145/3659943 – ident: ref_16 doi: 10.1109/IROS45743.2020.9341571 |
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| SubjectTerms | Adaptation adversarial attack affordable resilience Artificial intelligence Business metrics Comparative analysis concept drift dynamic deep neural networks Edge computing Efficiency Embedded systems fault injection Fault tolerance Methods Neural networks R&D Research & development robustness |
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| Title | Model and Method for Providing Resilience to Resource-Constrained AI-System |
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