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Cloud Versus Edge Deployment Strategies of Real-Time Face Recognition Inference by Anis Koubaa; Adel Ammar; Anas Kanhouch; Yasser AlHabashi is a Computer Science article available to read on EtoBox.

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Choosing the appropriate deployment strategy for any Deep Learning (DL) project in a production environment has always been the most challenging problem for industrial practitioners. There are several conflicting constraints and controversial approaches when it comes to deployment. Among these problems, the deployment on cloud versus the deployment on edge represents a common dilemma. In a nutshell, each approach provides benefits where the other would have limitations. This paper presents a real-world case study on deploying a face recognition application using MTCNN detector and FaceNet recognizer. We report the challenges faced to decide on the best deployment strategy. We propose three inference architectures for the deployment, including cloud-based, edge-based, and hybrid. Furthermore, we evaluate the performance of face recognition inference on different cloud-based and edge-based GPU platforms. We consider different models of Jetson boards for the edge (Nano, TX2, Xavier NX, Xavier AGX) and various GPUs for the cloud (GTX 1080, RTX 2080Ti, RTX 2070, and RTX 8000). We also investigate the effect of deep learning model optimization using TensorRT and TFLite compared to a stan

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Author

Anis Koubaa; Adel Ammar; Anas Kanhouch; Yasser AlHabashi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2022

Language

EN

Field

Computer Science (Physical Sciences)