Optimizing Neural Network For Deployment On Edge Devices

Keywords: Networks like convolution neural networks and their derivatives have contributed to the area of computer vision's explosive growth in recent years. Such a network however cannot be deployed on the edge device because of the high computational cost and memory requirements for model storage.Edge computing can address problems with latency connectivity cost and privacy but edge devices still face difficulties due to the deep learning model's high resource requirements. A big network-sized CNN model with more floating-point operations is required particularly for deep learning-basedapplications. In order to address those issues this study provides a strategy for deploying deep learning models to edge devices. The neural network has been optimized utilizing memory and computation-saving methods like pruning weight clusteringand quantization.

Abstract:
Networks like convolution neural networks and their derivatives have contributed to the area of computer vision's explosive growth in recent years. Such a network however cannot be deployed on the edge device because of the high computational cost and memory requirements for model storage.Edge computing can address problems with latency, connectivity, cost, and privacy, but edge devices still face difficulties due to the deep learning model's high resource requirements. A big network-sized CNN model with more floating-point operations is required, particularly for deep learning-basedapplications. In order to address those issues, this study provides a strategy for deploying deep learning models to edge devices. The neural network has been optimized utilizing memory and computation-saving methods like pruning, weight clusteringand quantization.

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