Fog Computing. Группа авторов

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it interacts with, but also recognize their facial emotions at the same time. These tasks all share the same data inputs and the limited resources on the edge device. How to effectively share the data inputs across concurrent deep learning tasks and efficiently utilize the shared resources to maximize the overall performance of all the concurrent deep learning tasks is challenging.

Illustration of data sharing mechanism for creating a data provider that is transparent to deep learning tasks and sits between them and the operating system.

      3.2.7 Offloading to Nearby Edges

      For edge devices that have extremely limited resources such as low-end Internet of Things (IoT) devices, they may still not be able to afford executing the most memory and computation-efficient DNN models locally. In such a scenario, instead of running the DNN models locally, it is necessary to offload the execution of DNN models. As mentioned in the introduction section, offloading to the cloud has a number of drawbacks, including leaking user privacy and suffering from unpredictable end-to-end network latency that could affect user experience, especially when real-time feedback is needed. Considering those drawbacks, a better option is to offload to nearby edge devices that have ample resources to execute the DNN models.

Image described by caption.

      3.2.8 On-device Training

      In common practice, DNN models are trained on high-end workstations equipped with powerful GPUs where training data are also located. This is the approach that giant AI companies such as Google, Facebook, and Amazon have adopted. These companies have been collecting a gigantic amount of data from users and use those data to train their DNN models. This approach, however, is privacy-intrusive, especially for mobile phone users because mobile phones may contain the users' privacy-sensitive data. Protecting users' privacy while still obtaining well-trained DNN models becomes a challenge.

      To address this challenge, we envision that the opportunity lies in on-device training. As computer resources in edge devices become increasingly powerful, especially with the emergence of AI chipsets, in the near future, it becomes feasible to train a DNN model locally on edge devices. By keeping all the personal data that may contain private information on edge devices, on-device training provides a privacy-preserving mechanism that leverages the compute resources inside edge devices to train DNN models without sending the privacy-sensitive personal data to the giant AI companies. Moreover, today, gigantic amounts of data are generated by edge devices such as mobile phones on a daily basis. These data contain valuable information about users and their personal preferences. With such personal information, on-device training is enabling training personalized DNN models that deliver personalized services to maximally enhance user experiences.

      Edge computing is revolutionizing the way we live, work, and interact with the world. With the recent breakthrough in deep learning, it is expected that in the foreseeable future, majority of the edge devices will be equipped with machine intelligence powered by deep learning. To realize the full promise of deep learning in the era of edge computing, there are daunting challenges to address.

      1 1 Shi, W., Cao, J., Zhang, Q. et al. (2016). Edge computing: vision and challenges. IEEE Internet of Things Journal 3 (5): 637–646.

      2 2 Shi, W. and Dustdar, S. (2016). The promise of edge computing. Computer 49 (5): 78–81.

      3 3 Satyanarayanan, M. (2017). The emergence of edge computing.


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