Towards Energy-Efficient Edge AI: A Novel Architecture for On-Device Large Language Models

We propose a new architectural design that significantly reduces the computational and energy footprint of large language models (LLMs), enabling their efficient deployment on edge devices. This breakthrough facilitates real-time, privacy-preserving AI applications in mobile computing, IoT, and embedded systems, addressing the critical need for sustainable and decentralized AI inference.