Energy-Efficient Multimodal Foundation Models for Real-Time Intelligent Manufacturing at the Edge
Keywords:
multimodal foundation models, edge intelligence, intelligent manufacturing, energy efficiency, real-time inference, sustainability, system governanceAbstract
The deployment of multimodal foundation models at the manufacturing edge has become a central research problem for next-generation industrial intelligence. These models can integrate visual, textual, acoustic, and operational data to support real-time quality inspection, predictive maintenance, human-machine collaboration, and adaptive process control. However, their computational intensity and energy consumption conflict with the constrained power, thermal, memory, and communication budgets of industrial edge environments. This paper presents a system-level analysis of energy-efficient multimodal foundation models for real-time intelligent manufacturing. It examines the structural trade-offs among model expressiveness, inference latency, energy consumption, and reliability. The discussion covers architectural compression, cross-modal semantic coordination, edge-cloud orchestration, hardware-aware adaptation, governance mechanisms, and sustainability implications. Rather than focusing on a single algorithm or benchmark, the paper develops a socio-technical perspective on how multimodal foundation models can be embedded within manufacturing infrastructures without undermining operational safety, data governance, or environmental goals. The analysis emphasizes that energy efficiency is not merely a hardware constraint but a design principle that must be integrated across model architecture, deployment strategy, and regulatory frameworks. The paper concludes by outlining future research directions for adaptive multimodal inference, collaborative edge learning, carbon-aware orchestration, and lifecycle governance in industrial artificial intelligence systems.
References
1. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998–6008.
2. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., Brynjolfsson, E., Buch, S., Card, D., Castellon, R., Chatterji, N., Chen, A., Creel, K., Davis, J. Q., Demszky, D., ... Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
3. Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning transferable visual models from natural language supervision. Proceedings of the 38th International Conference on Machine Learning, PMLR 139, 8748–8763.
4. Shi, W., Cao, J., Zhang, Q., Li, Y., & Xu, L. (2016). Edge computing: Vision and challenges. IEEE Internet of Things Journal, 3(5), 637–646.
5. Han, S., Mao, H., & Dally, W. J. (2016). Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv preprint arXiv:1510.00149.
6. Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
7. Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., & Kalenichenko, D. (2018). Quantization and training of neural networks for efficient integer-arithmetic-only inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2704–2713.
8. Chen, Ce, et al. "JuZhou 1.0 Technical Report: The First Edge-Native Text-to-Image Foundation Model Trained Entirely on China-Developed AI Accelerators." arXiv preprint arXiv:2606.28421 (2026).
9. McMahan, B., Moore, E., Ramage, D., Hampson, S., & Arcas, B. A. y. (2017). Communication-efficient learning of deep networks from decentralized data. Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54, 1273–1282.
10. Kang, Y., Hauswald, J., Gao, C., Rovinski, A., Mudge, T., Mars, J., & Tang, L. (2017). Neurosurgeon: Collaborative intelligence between the cloud and mobile edge. Proceedings of the 22nd International Conference on Architectural Support for Programming Languages and Operating Systems, 615–629.
11. Tao, F., Zhang, H., Liu, A., & Nee, A. Y. C. (2019). Digital twin-driven product design, manufacturing and service with big data. The International Journal of Advanced Manufacturing Technology, 94(9–12), 3563–3576.
12. Lee, J., Bagheri, B., & Kao, H.-A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.
13. Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., & Müller, K.-R. (Eds.). (2019). Explainable AI: Interpreting, explaining and visualizing deep learning. Springer.
14. Lu, Y., Zheng, H., Chand, S., Xia, W., Liu, Z., Xu, X., Wang, L., Qin, Z., & Bao, J. (2022). Outlook on human-centric manufacturing towards Industry 5.0. Journal of Manufacturing Systems, 62, 612–627.
15. Strubell, E., Ganesh, A., & McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 3645–3650.
16. Baltrusaitis, T., Ahuja, C., & Morency, L.-P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443.
17. Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156.
18. Stock, T., & Seliger, G. (2016). Opportunities of sustainable manufacturing in Industry 4.0. Procedia CIRP, 40, 536–541.
19. European Parliament and Council of the European Union. (2024). Regulation (EU) 2024/1689 laying down harmonised rules on artificial intelligence. Official Journal of the European Union, L series.
20. Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019). Continual lifelong learning with neural networks: A review. Neural Networks, 113, 54–71.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Advanced Artificial Intelligence Research
This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
