用硅光子芯片加速生成式AI，能效提升3倍、速度提升5.5倍。
Accelerating Diffusion Models for Generative AI Applications with Silicon Photonics
- 采用硅光子硬件替代传统电子芯片，优化扩散模型推理过程
- 实测能效比顶尖加速器高3倍，吞吐量提升5.5倍
- 适合需要低功耗高并发的生成式AI部署场景
扩散模型推动了生成式AI的发展，能够生成高度逼真的前沿合成数据。然而，这类模型依赖于计算密集的UNet和注意力机制进行迭代去噪，导致在传统电子平台上推理能耗过高。为此，本文提出一种基于硅光子技术的扩散模型加速器。实验表明，该光子加速器在能效上至少优于现有最先进加速器3倍，在吞吐量上提升5.5倍。
原文摘要 · Abstract (English)
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.
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