A Dynamic Deep Learning Ecosystem for Real-Time Cognitive Data Processing on Industrial Edge Devices

The capacity to manage cognitive data in real time is crucial in the rapidly increasing field of industrial edge computing. This criterion must be fully completed to proceed. Our research resulted in the creation of a new platform dubbed the Dynamic Deep Learning Ecosystem, or DDLE, which strives to achieve these goals. The Deep Learning Edge (DDLE) is a strong architecture that ensures deep learning models run as well as possible on edge devices. The dynamic data-driven learning environment (DDLE) includes approaches such as dynamic model scaling (DMS), edge-based pruning (EBP), and real-time adaptive inference (RAI). The entire goal of the operation is dependent on these algorithms accomplishing all this work. Deep learning models may face dynamic changes in complexity and size if DMS is used. The way these changes are implemented is determined in part by the type of data and the amount of processing capacity presently available. The proposed DDLE has been thoroughly tested, and the results demonstrate that it beats more standard approaches in certain crucial measures, such as accuracy and latency. The findings of this study highlight the necessity of deep learning at the edge as well as the potential benefits of DDLE. Furthermore, it demonstrates that DDLE can be a viable option for businesses looking for a real-time cognitive data processing solution.