Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

By En Li, Liekang Zeng, Zhi Zhou, Xu Chen. 2019

870 citations recorded by the source corpus.

As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challenging to run computation-intensive DNN-based tasks on mobile devices due to the limited computation resources. What’s worse, traditional cloud-assisted DNN inference is heavily hindered by the significant wide-area network latency, leading to poor real-time performance as well as low quality of user experience. To address these challenges, in this paper, we propose Edgent, a framework that leverages edge computing for DNN collaborative inference through device-edge synergy. Edgent exploits two design knobs: (1) DNN partitioning that adaptively partitions computation between device and edge for purpose of coordinating the powerful cloud resource and the proximal edge resource for real-time DNN inference; (2) DNN right-sizing that further reduces computing latency via early exiting inference at an appropriate intermediate DNN layer. In addition, considering the potential network fluctuation in real-world deployment, Edgent is properly design to specialize for both static and dynamic network environment. Specifically, in a static environment where the bandwidth changes slowly, Edgent derives the best configurations with the assist of regression-based prediction models, while in a dynamic environment where the bandwidth varies dramatically, Edgent generates the best execution plan through the online change point detection algorithm that maps the current bandwidth state to the optimal configuration. We implement Edgent prototype based on the Raspberry Pi and the desktop PC and the extensive experimental evaluations demonstrate Edgent’s effectiveness in enabling on-demand low-latency edge intelligence.

Subfields: Computer Networks and Communications, Computer Vision and Pattern Recognition, Information Systems, ML Systems & Hardware

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Citations in the corpus

Choose Your Weapon: Survival Strategies for Depressed AI Academics (2023, methodology): “This is often called edge AI [8], the operation of AI applications in devices of the physical world is possible when memory requirements are low and inference occurs rapidly.”
Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework (2020, methodology): “Otherwise, the device user will carry out model partitioning by keeping the input layers and its data samples locally on its device and offloading the remaining model layers to the edge for device-edge collaborative computing [14].”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, methodology): “For vertical edge inference, the dynamic computation partition and early existing scheme was proposed in [82] to accelerate the inference speed via edge device-server synergy.”