From Cloud to Edge: Rethinking Generative AI for Low-Resource Design Challenges

TL;DR

This paper explores the potential and challenges of adapting generative AI for design tasks in resource-constrained edge environments, emphasizing model efficiency, innovative algorithms, and real-world applications.

Contribution

It highlights the need for novel model compression and efficient algorithms to enable generative AI in low-resource settings for diverse design applications.

Findings

Identifies key challenges in deploying generative AI on edge devices.

Proposes potential approaches like model compression and efficient algorithms.

Discusses applications in healthcare, agriculture, and education.

Abstract

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.