Generative Artificial Intelligence (AI) has shown tremendous prospects in all aspects of technology, including design. However, due to its heavy demand on resources, it is usually trained on large computing infrastructure and often made available as a cloud-based service. In this position paper, we consider the potential, challenges, and promising approaches for generative AI for design on the edge, i.e., in resource-constrained settings where memory, compute, energy (battery) and network connectivity may be limited. Adapting generative AI for such settings involves overcoming significant hurdles, primarily in how to streamline complex models to function efficiently in low-resource environments. This necessitates innovative approaches in model compression, efficient algorithmic design, and perhaps even leveraging edge computing. The objective is to harness the power of generative AI in creating bespoke solutions for design problems, such as medical interventions, farm equipment maintenance, and educational material design, tailored to the unique constraints and needs of remote areas. These efforts could democratize access to advanced technology and foster sustainable development, ensuring universal accessibility and environmental consideration of AI-driven design benefits.

翻译：生成式人工智能在技术的各个领域（包括设计领域）展现出巨大潜力。然而，由于其资源需求庞大，通常需在大型计算基础设施上训练，并多作为云服务提供。本立场论文探讨了边缘端（即内存、计算能力、电池能耗和网络连接可能受限的资源约束场景）生成式AI在设计应用中的潜力、挑战与可行方案。适配生成式AI至此类场景需克服显著障碍，核心在于如何精简复杂模型，使其在低资源环境下高效运行。这要求我们在模型压缩、高效算法设计乃至边缘计算应用方面开展创新性探索。目标旨在利用生成式AI为设计问题（如医疗干预、农用设备维护及教育材料设计）打造定制化解决方案，充分兼顾偏远地区的独特限制与实际需求。这些努力有望促进先进技术的普惠化，推动可持续发展，确保生成式AI设计成果的通用可及性与环境友好性。