From Cloud to Edge: Rethinking Generative AI for Low-Resource Design Challenges
It addresses the problem of making generative AI accessible in low-resource settings for applications in remote areas, though it presents a conceptual framework rather than concrete implementations.
This position paper examines the challenges and potential approaches for adapting generative AI to resource-constrained edge environments, aiming to enable bespoke design solutions for applications like medical interventions and farm equipment maintenance in remote areas.
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.