  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.
