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.

翻译：扩散模型通过其生成高度逼真、最先进的合成数据的内在能力，彻底改变了生成式人工智能领域。然而，这些模型在计算密集的层（如UNet和注意力机制）上采用了迭代去噪过程。这导致在传统电子平台上推理能耗极高，因此，以可持续的方式加速这些模型的需求日益凸显。为应对这一挑战，我们提出了一种基于硅光子学的新型扩散模型加速器。实验评估表明，与最先进的扩散模型加速器相比，我们的光子加速器在能效上至少提升了3倍，吞吐量提高了5.5倍。