Cloud Memory Enabled Code Generation via Online Computing for Seamless Edge AI Operation

This paper introduces an innovative architecture designed to enhance the execution of Artificial Intelligence (AI) software on edge devices, which are often constrained by limited hardware resources. The core of proposal is to dynamically adapt AI models through server-mediated parameter updates and learning, thus allowing edge devices to efficiently process AI tasks in real-time and adapt to various operational conditions. By leveraging the computational power of cloud resources for the heavy lifting of AI model training, the computational burden on edge devices is alleviated, enabling them to focus on inference tasks with updated models. This approach significantly improves the operational efficiency and adaptability of edge computing in AI applications. Our architecture employs server-based emulation to monitor and dynamically update edge devices, ensuring their execution is optimized for current conditions. Experimental results demonstrate a substantial reduction in operational time up to 75% compared to traditional edge devices without accelerators and 49 % when compared to devices equipped with accelerators. Moreover, proposed model shows an ability to improve accuracy by 20 % in scenarios with biased inputs through continuous learning and parameter updating, highlighting its adaptability to changing environments. This research contributes to the field of edge computing by demonstrating a viable solution for deploying sophisticated AI models in resource-constrained environments. By offloading computationally intensive tasks to the cloud, proposed architecture ensures that edge devices can operate more efficiently and handle a broader range of AI applications. This study not only underscores the potential of integrating cloud and edge computing to overcome the limitations of edge devices but also opens new avenues for future research in intelligent edge computing systems.