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

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…

• NN
  National Natural Science Foundation of China

  Awards: 61972432, 61802449, U1711265
• FR
  Fundamental Research Funds for the Central Universities

  Award: 17lgjc40