Edge Device Training Testbed

Large Language Models (LLMs) have demonstrated remarkable performance across various natural language processing tasks. However, deploying them on embedded platforms remains a significant challenge due to hardware constraints and limited energy availability. This project addresses the growing demand for secure, autonomous AI capabilities in edge computing environments—where reliance on cloud infrastructure is often impractical due to privacy concerns, latency requirements, or unreliable connectivity. Our objective is to evaluate, adapt, and implement a Small Language Model (SLM) optimized for resource-constrained embedded systems, with a strong emphasis on offline functionality, ultra-low power consumption, very low computational resources, and maintaining a responsive user experience. We specifically aim to implement this Small Language Model (SLM) within a smart ashtray application, designed to assist and support smokers in their smoking cessation journey by providing personalized advice and autonomous monitoring directly on the device.

HE-Arc