Розмір шрифта:
LIQUID NEURAL NETWORKS: DYNAMIC ADAPTIVITY AND EDGE INTELLIGENCE
Остання редакція: 2026-05-26
Анотація
This study explores the integration of Liquid Neural Networks (LNNs) as a transformative framework for real-time, decentralized intelligence. Unlike traditional static models, LNNs utilize continuous-time dynamics to adapt their internal parameters on the fly. The research compares the efficiency of LNNs against standard architectures, highlighting how their compact parameter count – often several orders of magnitude smaller than Transformers – enables high-performance processing on edge devices with minimal power consumption. Furthermore, it examines how the inherent interpretability and "liquid" nature of these networks allow for seamless personalization and robust performance in noisy, unpredictable environments without the need for centralized data retraining.
Ключові слова
liquid neural networks, continuous-time AI, edge computing, real-time adaptation, parameter efficiency, bio-inspired intelligence
Посилання
1. Hasani, R., Lechner, M., Amini, A. et al. (2022) Closed-form continuous-time neural networks. Nat Mach Intell 4, 992–1003.
2. Kristiani, E., Verma, V. K., & Yang, C.-T. (2026). Deploying LLM Transformer on Edge Computing Devices: A Survey of Strategies, Challenges, and Future Directions. AI, 7(1), 15.
3. Makram Chahine et al, (2025). The Curious Case of In-Training Compression of State Space Models, arXiv.
4. Nykyporets, S. S., Kot, S. O., Boiko, Y. V., Melnyk, M. B., & Chopliak, V. V. (2024). Advanced integration of virtual information environments (VIEs) in contemporary educational methodologies. Society and National Interests. Series “Education/Pedagogy”, 4(4), 139-154. https://doi.org/10.52058/3041-1572-2024-4(4)-139-154.
5. Kravchenko, K., Ketsyk-Zinchenko, U., Suduk, I., Nykyporets, S., & Cherednychenko, V. (2025). Effectiveness of online platforms in developing language skills of higher education students. Revista Eduweb, 19(3), 303-314. https://doi.org/10.46502/issn.1856-7576/2025.19.03.19.
2. Kristiani, E., Verma, V. K., & Yang, C.-T. (2026). Deploying LLM Transformer on Edge Computing Devices: A Survey of Strategies, Challenges, and Future Directions. AI, 7(1), 15.
3. Makram Chahine et al, (2025). The Curious Case of In-Training Compression of State Space Models, arXiv.
4. Nykyporets, S. S., Kot, S. O., Boiko, Y. V., Melnyk, M. B., & Chopliak, V. V. (2024). Advanced integration of virtual information environments (VIEs) in contemporary educational methodologies. Society and National Interests. Series “Education/Pedagogy”, 4(4), 139-154. https://doi.org/10.52058/3041-1572-2024-4(4)-139-154.
5. Kravchenko, K., Ketsyk-Zinchenko, U., Suduk, I., Nykyporets, S., & Cherednychenko, V. (2025). Effectiveness of online platforms in developing language skills of higher education students. Revista Eduweb, 19(3), 303-314. https://doi.org/10.46502/issn.1856-7576/2025.19.03.19.
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