Energy-Efficient Multimodal Foundation Models for Real-Time Visual Content Generation on Edge Devices
Keywords:
multimodal foundation models, energy efficiency, edge computing, real-time visual generation, model compression, hardware-software co-designAbstract
The rapid proliferation of multimodal foundation models has enabled unprecedented capabilities in visual content generation, yet their deployment on edge devices is severely constrained by energy budgets, thermal envelopes, and latency requirements. This paper provides a system-level analysis of energy-efficient multimodal foundation models designed for real-time visual synthesis at the edge. We examine the structural trade-offs between model expressiveness and power consumption, survey architectural innovations in model compression, distillation, and quantization, and discuss hardware-software co-design strategies that leverage heterogeneous accelerators and edge-native compute fabrics. The deployment infrastructure is analyzed through the lens of the edge-cloud continuum, workload partitioning, and orchestration for low-latency inference. Beyond technical optimization, the paper addresses sociotechnical dimensions including robustness, fairness, and bias in generative outputs, lifecycle sustainability accounting, and the evolving landscape of governance and policy instruments that shape responsible edge AI. Throughout the discussion, cross-domain comparisons between mobile vision, data center generative models, and emerging edge-native foundations illuminate the systemic challenges and opportunities. The paper concludes with forward-looking perspectives on standardization, regulatory frameworks, and the long-term viability of energy-proportional multimodal intelligence at the extreme edge, arguing that sustainable visual AI demands a holistic integration of model design, accelerator ecosystems, and socio-environmental accountability.
References
1. Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., … & Liang, P. (2021). On the opportunities and risks of foundation models. arXiv preprint arXiv:2108.07258.
2. 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.
3. Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., & Zhang, J. (2019). Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proceedings of the IEEE, 107(8), 1738–1762.
4. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10684–10695.
5. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with CLIP latents. arXiv preprint arXiv:2204.06125.
6. 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.
7. Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., … & Dean, J. (2021). Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.
8. Han, S., Mao, H., & Dally, W. J. (2015). Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. arXiv preprint arXiv:1510.00149.
9. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., … & Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
10. Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L.-C. (2018). MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4510–4520.
11. Tan, M., & Le, Q. V. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning, 6105–6114.
12. Hinton, G., Vinyals, O., & Dean, J. (2015). Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
13. Salimans, T., & Ho, J. (2022). Progressive distillation for fast sampling of diffusion models. Proceedings of the International Conference on Learning Representations.
14. Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017). Efficient processing of deep neural networks: A tutorial and survey. Proceedings of the IEEE, 105(12), 2295–2329.
15. Chen, C., Wang, C., Li, Y., Wan, Z., Geng, M., Xiao, J., ... & Peng, Y. (2026). 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.
16. Li, J., Li, D., Savarese, S., & Hoi, S. (2023). BLIP-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. Proceedings of the 40th International Conference on Machine Learning, 19730–19742.
17. Liu, H., Li, C., Wu, Q., & Lee, Y. J. (2023). Visual instruction tuning. Advances in Neural Information Processing Systems, 36.
18. Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. (2021). A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 1–35.
19. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., … & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707.
20. Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019). Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700.
21. Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399.
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.
