# Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics

arXiv: https://arxiv.org/abs/2603.07626 · HTML: https://mlsanity.com/paper/2603.07626/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 18 of its 34 references that are in the map.
- First posted on arXiv 8 Mar 2026.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 18 of 18)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DeepCache: Accelerating Diffusion Models for Free](https://mlsanity.com/paper/2312.00858/) (2023)
- [Q-Diffusion: Quantizing Diffusion Models](https://mlsanity.com/paper/2302.04304/) (2023)
- [A Note on the Inception Score](https://mlsanity.com/paper/1801.01973/) (2018)
- [Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations](https://mlsanity.com/paper/2304.11267/) (2023)
- [A Survey on Silicon Photonics for Deep Learning](https://mlsanity.com/paper/2101.01751/) (2021)
- [ROBIN: A Robust Optical Binary Neural Network Accelerator](https://mlsanity.com/paper/2107.05530/) (2021)
- [Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator](https://mlsanity.com/paper/2305.19533/) (2023)
- [RecLight: A Recurrent Neural Network Accelerator with Integrated Silicon Photonics](https://mlsanity.com/paper/2209.00084/) (2022)
- [TRON: Transformer Neural Network Acceleration with Non-Coherent Silicon Photonics](https://mlsanity.com/paper/2303.12914/) (2023)
- [SONIC: A Sparse Neural Network Inference Accelerator with Silicon Photonics for Energy-Efficient Deep Learning](https://mlsanity.com/paper/2109.04459/) (2021)
- [A Silicon Photonic Accelerator for Convolutional Neural Networks with Heterogeneous Quantization](https://mlsanity.com/paper/2205.11244/) (2022)
- [PCNNA: A Photonic Convolutional Neural Network Accelerator](https://mlsanity.com/paper/1807.08792/) (2018)
- [GHOST: A Graph Neural Network Accelerator using Silicon Photonics](https://mlsanity.com/paper/2307.01782/) (2023)
- [SafeLight: Enhancing Security in Optical Convolutional Neural Network Accelerators](https://mlsanity.com/paper/2411.16712/) (2024)
- [OPIMA: Optical Processing-In-Memory for Convolutional Neural Network Acceleration](https://mlsanity.com/paper/2407.08205/) (2024)
- [PhotoGAN: Generative Adversarial Neural Network Acceleration with Silicon Photonics](https://mlsanity.com/paper/2501.13828/) (2025)

## Abstract (arXiv)

Diffusion models have revolutionized generative AI, with their inherent capacity to generate highly realistic state-of-the-art synthetic data. However, these models employ an iterative denoising process over computationally intensive layers such as UNets and attention mechanisms. This results in high inference energy on conventional electronic platforms, and thus, there is an emerging need to accelerate these models in a sustainable manner. To address this challenge, we present a novel silicon photonics-based accelerator for diffusion models. Experimental evaluations demonstrate that our photonic accelerator achieves at least 3x better energy efficiency and 5.5x throughput improvement compared to state-of-the-art diffusion model accelerators.