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：生成式人工智能在设计等各技术领域展现出巨大潜力。然而，由于其对资源的巨大需求，通常需在大型计算基础设施上训练，并常以云端服务形式提供。在本立场论文中，我们探讨了在边缘侧实现生成式AI设计的潜力、挑战与可行方案——即在内存、计算、能源（电池）和网络连接可能受限的资源约束环境中。将生成式AI适配至此类场景需克服重大障碍，核心在于如何精简复杂模型以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等方面提出创新方法。其目标是利用生成式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，以适应偏远地区独特的约束与需求。这些努力有望推动先进技术的民主化普及，促进可持续发展，确保AI驱动设计在实现普惠性的同时兼顾生态环境影响。