# Seamless Optical Cloud Computing across Edge-Metro Network for Generative AI

arXiv: https://arxiv.org/abs/2412.12126 · HTML: https://mlsanity.com/paper/2412.12126/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 59 references that are in the map.
- First posted on arXiv 4 Dec 2024.

## Builds on (most cited first, 15 of 15)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [Towards artificial general intelligence via a multimodal foundation model](https://mlsanity.com/paper/2110.14378/) (2021)
- [Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit](https://mlsanity.com/paper/2008.11659/) (2020)
- [A Survey on Large-scale Machine Learning](https://mlsanity.com/paper/2008.03911/) (2020)
- [A Survey on Data Augmentation in Large Model Era](https://mlsanity.com/paper/2401.15422/) (2024)
- [Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment](https://mlsanity.com/paper/2111.06862/) (2021)
- [Next Generation Cloud Computing: New Trends and Research Directions](https://mlsanity.com/paper/1707.07452/) (2017)
- [Silicon photonic-electronic neural network for fibre nonlinearity compensation](https://mlsanity.com/paper/2110.12833/) (2021)
- [A system-on-chip microwave photonic processor solves dynamic RF interference in real time with picosecond latency](https://mlsanity.com/paper/2306.14727/) (2023)
- [Asymmetrical estimator for training encapsulated deep photonic neural networks](https://mlsanity.com/paper/2405.18458/) (2024)
- [Edge-guided inverse design of digital metamaterial-based mode multiplexers for high-capacity multi-dimensional interconnect](https://mlsanity.com/paper/2410.07572/) (2024)

## Built on by (most cited first, 1 of 1)

- [Optical Computation-in-Communication enables low-latency, high-fidelity perception in telesurgery](https://mlsanity.com/paper/2510.14058/) (2025)

## Abstract (arXiv)

The rapid advancement of generative artificial intelligence (AI) in recent years has profoundly reshaped modern lifestyles, necessitating a revolutionary architecture to support the growing demands for computational power. Cloud computing has become the driving force behind this transformation. However, it consumes significant power and faces computation security risks due to the reliance on extensive data centers and servers in the cloud. Reducing power consumption while enhancing computational scale remains persistent challenges in cloud computing. Here, we propose and experimentally demonstrate an optical cloud computing system that can be seamlessly deployed across edge-metro network. By modulating inputs and models into light, a wide range of edge nodes can directly access the optical computing center via the edge-metro network. The experimental validations show an energy efficiency of 118.6 mW/TOPs (tera operations per second), reducing energy consumption by two orders of magnitude compared to traditional electronic-based cloud computing solutions. Furthermore, it is experimentally validated that this architecture can perform various complex generative AI models through parallel computing to achieve image generation tasks.