发表机构
The University of Edinburgh（爱丁堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文从软硬件视角指出视觉生成式AI发展中硬件被动适配模型的问题，提出软硬件协同设计方法，以实现生成式AI在边缘等多平台的可持续部署。
AI 中文摘要
视觉生成式人工智能（AI）已成为深度学习领域发展最迅速的方向之一。多模态模型的爆发式发展使其与运行在大型数据中心的文本到图像应用广泛关联。然而，在自动驾驶汽车、农业传感器、移动设备等受严格硬件约束的边缘应用中，同样需要视觉生成模型。在本视角文章中，我们指出视觉生成式AI的进展由输出质量驱动，硬件则被动进化以适应不断增长的模型需求。我们在一系列加速器平台上量化了这些模型的参数成本和能效，并将四类生成式模型家族映射到七个现实应用领域。最后，我们倡导软硬件协同设计方法，从设计之初就考虑部署约束，确保“合适的模型”在“合适的硬件”上运行，以服务“合适的应用”，使生成式AI的部署在更广泛的平台上具备可持续性和可访问性。
英文摘要
Vision generative artificial intelligence (AI) has emerged as one of the most rapidly advancing areas of deep learning. The explosion of multimodal models has made them widely associated with text-to-image applications running on large datacentres. However, vision generative models are equally needed in applications that operate under strict hardware constraints at the edge, including autonomous vehicles, agricultural sensors, and mobile devices. In this Perspective, we argue that progress in vision generative AI has been driven by output quality, with hardware evolving reactively to accommodate growing model demands. We quantify the parameter cost and energy efficiency of these models across a range of accelerator platforms, and map four generative model families against seven real-world application domains. Finally, we advocate a software-hardware co-design approach, where deployment constraints are considered from the start of the design process, ensuring that the "right model" runs on the "right hardware" to serve the "right application", making generative AI deployment sustainable and accessible across a much broader range of platforms.
