convergence of edge computing and deep learning: a comprehensive survey

Our implementation of DeepCache works with unmodified deep learning models, requires zero developer's manual effort, and is therefore immediately deployable on off-the-shelf mobile devices. With the rise of IoT, 5G networks, and real-time analytics, the edge has expanded into a greater and even more dominant part of the computing infrastructure equation. Although edge computing is an appealing technology to compensate for stringent latency related issues, its deployment engenders new challenges. Xu Chen's 14 research works with 186 citations and 1,580 reads, including: Artificial Intelligence Inference in Edge. By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing can be mutually beneficial. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. As it reaches the client, it is captured off the wire and it goes through the client processor cache on its way to the client's main memory. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Leung , Dusit Niyato , Xueqiang Yan , Xu Chen (Submitted on 19 Jul 2019 ( v1 ), last revised 28 Jan 2020 (this version, v3)) Part of the Lecture Notes in Computer Science book series (LNCS, volume 12338) Abstract Thanks to recent advancements in edge computing, the traditional centralized cloud-based approach to deploy Artificial Intelligence (AI) techniques will be soon replaced or complemented by the so-called edge … • The exploration of open research challenges. Lim et al. Real-time image-based object tracking from live video is of great importance for several smart city applications like surveillance, intelligent traffic management and autonomous driving. 09/02/2020 ∙ by Hamza Ali Imran, et al. retrieval methods, statistical learning and machine learning … Moreover, ECRT can minimize the power consumption of IoT devices while taking into consideration the dynamic network environment and user requirement on end to end delay. Neural network learning algorithms are employed to analyze the network and compute resource required by each network node operates as a whole network resource allocation service. A Survey of Mobile Edge Computing in the Industrial Internet. Simulation results show that the proposed RL based offloading scheme reduces the energy consumption, computation delay and task drop rate and thus increases the utility of the IoT device in the dynamic MEC in comparison with the benchmark offloading schemes. As an important enabler broadly changing people’s lives, from face recognition to ambitious smart factories and cities, developments of artificial intelligence, Access scientific knowledge from anywhere. ∙ 41 ∙ share . Results indicate that our proposed model can save average By focusing on deep learning as the most representative technique of AI, this book provides a comprehensive overview of how AI services are being applied to the network edge near the data sources, and demonstrates how AI and edge computing … In this survey, we highlight the role of edge computing in realizing the vision of smart cities. First, we analyze the evolution of edge computing paradigms. Meanwhile, there are some new problems to decrease the accuracy, such as the potential leakage of user privacy and mobility of user data. One solution is to offload DNN computations from the client device to nearby edge servers [1] to request an execution of the DNN computations with their powerful hardware. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. We measure the performance as seen by the user, and the cost of running three different MXNet [] trained deep learning models on the AWS Lambda serverless computing platform. Numerical results illustrate that our proposed algorithm for unknown CSI outperforms other schemes, such as Local Processing and Random Assignment, and achieves up to 87:87% average long-term payoffs compared to the perfect CSI case. It further realizes a distributed work stealing approach to enable dynamic workload distribution and balancing at inference runtime. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. This paper considers MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to be selected for computation offloading. Using the numerical simulations, we demonstrate the learning capacity of the proposed algorithm and analyze the end-to-end service latency. As an important enabler broadly changing people's lives, from face recognition to ambitious smart factories and cities, artificial intelligence (especially deep learning) applications and services have experienced a thriving development process. In this paper, we propose DeepThings, a framework for adaptively distributed execution of CNN-based inference applications on tightly resource-constrained IoT edge clusters. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. (BAA) results in dramatical communication-latency reduction compared with the conventional orthogonal access (i.e., OFDMA). The convergence of mobile edge computing (MEC) to the current Internet of Things (IoT) environment enables a great opportunity to enhance massive IoT data transmission. DeepThings employs a scalable Fused Tile Partitioning (FTP) of convolutional layers to minimize memory footprint while exposing parallelism. Wireless powered mobile-edge computing (MEC) has recently emerged as a promising paradigm to enhance the data processing capability of low-power networks, such as wireless sensor networks and internet of things (IoT). Convergence of edge computing and deep learning: A comprehensive survey X Wang, Y Han, VCM Leung, D Niyato, X Yan, X Chen IEEE Communications Surveys & Tutorials 22 (2), 869-904 , 2020 The proposed model is compared with DQL-EES on EdgeCloudSim in terms of energy saving and training time. The core idea is that the network controller makes intelligent decisions on UE communication modes and processors’ on-off states with precoding for UEs in C-RAN mode optimized subsequently, aiming at minimizing long-term system power consumption under the dynamics of edge cache states. We also present techniques for NN algorithm exploration to develop light-weight models suitable for resource constrained systems, using keyword spotting as an example.

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