Research on Neural Network Model Generation and Deployment for Edge Intelligence
The exponential growth of diverse terminal devices,driven by advancements in mobile computing,5th Generation mobile communication(5G),and Internet of Things(IoT)technologies,has generated vast volumes of semi-structured and unstructured data,as these devices connect to networks.Neural networks are widely applicable because of their unique advantages in mining data.To enhance data processing capabilities and inference accuracy,neural network models are often designed to be complex and thus consume substantial computational resources in both storage and execution.However,edge devices typically have limited computational resources and cannot satisfy the storage and inference requirements of complex neural network models.Therefore,they often rely on cloud computing centers to perform these tasks.This cloud-based collaboration can lead to increased response latency and network bandwidth consumption,incurring potential risks such as the infringement of user privacy through data leakage.To address these issues,this paper introduces Neural network Generation and Deployment(NGD),a method for rapidly generating and deploying tailored neural network models on edge devices,whereby neural network models that match the hardware configuration of edge devices and specific computational task requirements are generated and rapidly deployed onto target devices for local inference.Experiments are conducted on three typical edge devices,and the results demonstrate the effectiveness of NGD in generating and deploying models,affirming its practicality and effectiveness.
万方数据
维普
