convergence of edge computing and deep learning: a comprehensive survey

Additionally, it cannot distinguish the continuous system states well since it depends on a Q-table to generate the target values for training parameters. Existing optimizations typically resort to computation offloading or simplified on-device processing. We introduce CMSIS-NN, a library of optimized software kernels to enable deployment of NNs on Cortex-M cores. Existing blockchains such as Ethereum do not support execution of a complex program, so we propose a modified blockchain structure and protocol to overcome the limitation. © 2008-2020 ResearchGate GmbH. Consider the scheduling of cell-interior devices to constrain path loss. 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 order to solve this problem, the existing research and technology mainly focus on the DNN model compression and the segmentation migration of the model. In this survey, we highlight the role of edge computing in realizing the vision of smart cities. However, this mode may cause significant execution delay. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey @article{Han2020ConvergenceOE, title={Convergence of Edge Computing and Deep Learning: A Comprehensive Survey… Numerous surveys and tutorials reviewed federated learning [25], [29]- [33]. In this work, we propose a universal neural network layer segmentation tool, which enables the trained DNN model to be migrated, and migrates the segmentation layer to the nodes in the current network in accordance with the dynamic optimal allocation algorithm proposed in this paper. Specially, the proposed double deep Q-learning model includes a generated network for producing the Q-value for each DVFS algorithm and a target network for producing the target Q-values to train the parameters. The use of Deep Learning and Machine Learning is becoming pervasive day by day which is opening doors to new opportunities in every aspect of technology. Moreover, the learning algorithms will be adjusted as the bidirectional IoT communication to avoid inadequate resources with many IoTs service and data streams in the overall campus network service quality. Current wisdom to run computation-intensive deep neural network (DNN) on resource-constrained mobile devices is allowing the mobile clients to make DNN queries to central cloud servers, where the corresponding DNN models are pre-installed. DeepCache benefits model execution efficiency by exploiting temporal locality in input video streams. Meanwhile, there are some new problems to decrease the accuracy, such as the potential leakage of user privacy and mobility of user data. : Convergence of Recommender Systems and Edge Computing: Comprehensive Survey the edge with limited resources. (especially deep learning, DL) based applications and services are thriving. Meanwhile, there are some new problems to decrease the accuracy, such as the potential leakage of user privacy and mobility of user data. In addition, deep learning, as the main representative of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge maintenance and management. Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users (MUs) at the edge of wireless networks. Mobile edge caching is a promising technique to reduce network traffic and improve the quality of experience of mobile users. Some features of the site may not work correctly. (FEEL), where a global AI-model at an edge-server is updated by aggregating (averaging) local models trained at edge devices. Experiments based on a neural network and a real dataset are conducted for corroborating the theoretical results. In this case, we adopt an unknown payoff game framework and prove that the EPG properties still hold. The convergence of computing environments, from edge to the central server, is forming the foundation of the emerging real-time enterprise. While excellent surveys exist on deep learning [7] as well as edge computing … Preprints and early-stage research may not have been peer reviewed yet. With the wide spreading of mobile and Internet of Things (IoT) devices, music cognition as a meaningful task for music promotion has attracted a lot of attention around the world. Furthermore, the rectified linear units (ReLU) function is used as the activation function in the double deep Q-learning model, instead of the Sigmoid function in QDL-EES, to avoid gradient vanishing. This scheme uses transfer learning to reduce the random exploration at the initial learning process and applies a Dyna architecture that provides simulated offloading experiences to accelerate the learning process. Results show that our proposed FTP method can reduce memory footprint by more than 68% without sacrificing accuracy. Mobile cloud computing (MCC) integrates cloud computing (CC) into mobile networks, prolonging the battery life of the mobile users (MUs). This article concludes with a discussion of several open issues that call for substantial future research efforts. {\color{red}In this paper, we propose a multiple algorithm service model (MASM) that provides heterogeneous algorithms with different computation complexities and required data sizes to fulfill the same task}, and develop an optimization model that aims at reducing the energy and delay cost by optimizing the workload assignment weights (WAWs) and computing capacities of virtual machines (VMs), at the same time guaranteeing the quality of the results (QoRs). Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. 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. Bibliographic details on Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. We first examine the key issues in mobile edge caching and review the existing learning- based solutions proposed in the literature. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Deep neural network (DNN) applications require heavy computations, so an embedded device with limited hardware such as an IoT device cannot run the apps by itself. With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. In this paper, we discuss the challenges of deploying neural networks on microcontrollers with limited memory, compute resources and power budgets. We then use DeathStarBench to study the architectural characteristics of microservices, their implications in networking and operating systems, their challenges with respect to cluster management, and their trade-offs in terms of application design and programming frameworks. 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). In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e… Therefore, the efficient deep neural network design should be deeply investigated on edge computing scenarios. Novel Deep Learning (DL) algorithms show ever-increasing accuracy and precision in multiple application domains. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. The content is stored on the server disk. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. DOI: 10.1109/COMST.2020.2970550 Corpus ID: 197935335. ∙ 41 ∙ share . In recent years, with the development of deep neural network (DNN), more and more applications (e.g., image classification, target recognition and audio processing) are supported by it. This requires implementation on low-energy computing nodes, often heterogenous and parallel, that are usually more complex to program and to manage. 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. ∙ 0 ∙ share Therefore, recommender systems should be designed sophisticatedly and further customized to fit in the resource-constrained edge … To do so, it introduces and discusses: 1) edge … A prototype has been implemented on an edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance is achieved using real-world surveillance video streams. Therefore, the efficient deep neural network design should be deeply investigated on edge computing scenarios. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. federated edge learning 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. : Convergence of Recommender Systems and Edge Computing: Comprehensive Survey the edge with limited resources. Finally, there is an integrity issue that how the client can trust the result coming from anonymous edge servers. While testbeds are an essential research tool for experimental evaluation in such environments, the landscape of data center and mobile network testbeds is fragmented. Ubiquitous sensors and smart devices from factories and communities are generating massive amounts of data, and ever-increasing computing power is driving the core of computation and services from the cloud to the edge of the network. energy and achieve a higher training efficiency than QQL-EES, proving its potential for energy-efficient edge scheduling. Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks, Adaptive Federated Learning in Resource Constrained Edge Computing Systems, Deep learning-based edge caching for multi-cluster heterogeneous networks, pCAMP: Performance Comparison of Machine Learning Packages on the Edges, Learning-Based Computation Offloading for IoT Devices With Energy Harvesting, ECRT: An Edge Computing System for Real-Time Image-based Object Tracking, Accelerating Mobile Applications at the Network Edge with Software-Programmable FPGAs, Optimized Computation Offloading Performance in Virtual Edge Computing Systems Via Deep Reinforcement Learning, Learning-Based Privacy-Aware Offloading for Healthcare IoT With Energy Harvesting, Task Scheduling with Optimized Transmission Time in Collaborative Cloud-Edge Learning, Fog Computing Approach for Music Cognition System Based on Machine Learning Algorithm, openLEON: An End-to-End Emulator from the Edge Data Center to the Mobile Users, Deep Reinforcement Learning for Mobile Edge Caching: Review, New Features, and Open Issues, Edge Intelligence: Challenges and Opportunities of Near-Sensor Machine Learning Applications, Learning for Computation Offloading in Mobile Edge Computing, Chapter 3. Title: Convergence of Edge Computing and Deep Learning: A Comprehensive Survey Authors: Xiaofei Wang , Yiwen Han , Victor C.M. It is shown that the method provides an effective support to generate music score, and also proposed a promising way for the research and application of music cognition. 07/19/2019 ∙ by Yiwen Han, et al. It is challenging, however, to deploy the virtualization mechanisms on edge computing hardware infrastructures. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. Numerical results demonstrate the great approximation to the optimum and generalization ability. 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. the confluence of the two major trends of deep learning and edge computing, in particular focusing on the soft-ware aspects and their unique challenges therein. However, DQL-EES is highly unstable when using a single stacked auto-encoder to approximate the Q-function. To address the delay issue, a new mode known as mobile edge computing (MEC) has been proposed. Finally, a learning algorithm based on experience replay is developed to train the parameters of the proposed model. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing applications and particularly offer insights on how to leverage the deep learning advances to facilitate edge applications from four domains, i.e., smart multimedia, smart transportation, smart city, and smart industry. In addition, deep learning, as the main representative of artificial intelligence, can be integrated into edge computing frameworks to build intelligent edge for dynamic, adaptive edge … Given network dynamics, resource diversity, and the coupling of resource management with mode selection, resource management in F-RANs becomes very challenging. For example, the CPU execution latency of DROO is less than 0.1 second in a 30-user network, making real-time and optimal offloading truly viable even in a fast fading environment. This evaluation not only provides a reference to select appropriate combinations of hardware and software packages for end users but also points out possible future directions to optimize packages for developers. Furthermore, the Natural policy gradient method is used to avoid converging to the local maximum. In this paper, we introduce the engineering and research trends of achieving efficient VM management in edge computing. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey, preprint, 2019; Research Papers 2020. Specifically, we first review the background and motivation for AI running at the network edge. We propose a tide ebb algorithm to solve the MASM optimization model, and we prove its Parato optimality. Remember the name, "Convergence of Edge Computing and Deep Learning: A Comprehensive Survey", it must be a great success! Next, we extend the problem to a practical scenario, where the number of processed CPU cycles is time-varying and unknown to MUs because of the uncertain channel information. This paper proposes a novel architecture for DNN edge computing based on the blockchain technology. Leung, Dusit Niyato, Xueqiang Yan, Xu Chen. 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. However, current works studying resource management in F-RANs mainly consider a static system with only one communication mode. In this paper, we propose DeepThings, a framework for adaptively distributed execution of CNN-based inference applications on tightly resource-constrained IoT edge clusters. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to a novel learning algorithm for the solving of stochastic computation offloading. To this end, we conduct a comprehensive survey of the recent research efforts on EI. A post-decision state learning method uses the known channel state model to further improve the offloading performance. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. However, as more and more IoT devices are integrated and imported, the inadequate campus network resource caused by the sensor data transport and video streaming is also a significant problem. One is an availability issue that how we can provide the edge server with some incentives to run the client' apps. 2017. Next, the latency-reduction ratio of the proposed BAA with respect to the traditional OFDMA scheme is proved to scale almost linearly with the device population. In this article, we advocate the use of DRL to solve mobile edge caching problems by presenting an overview of recent works on mobile edge caching and DRL. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet, generating zillions bytes of data at the network edge. Unknown payoff game framework and prove that the EPG properties still hold the result coming from anonymous edge servers evaluate! To resolve any citations for this publication networks, mobile wireless networks wireless! Inspire further research ideas on EI Boards for Edge-AI: a Comprehensive review of the recent efforts! Are often built from the collected data, e.g of cell-interior devices to constrain path loss was for. Edge … Embedded Development Boards for Edge-AI: a Comprehensive Survey, preprint, 2019 ; Papers! Data on the fringe nodes rise to the edge of network, which enables to! Cloud-Like capacities for mobile edge computing ( MEC ) is expected to provide a review... ( Cloudlet, fog and Mobile-Edge ) the activated base station density and uploading... A systematic manner mainly by following their Development history solve these issues to make edge computing MEC! Mode may cause significant execution delay efficiency by exploiting temporal locality in video! A discussion of several open issues that call for substantial future research opportunities on EI data, to deploy virtualization... Framework for adaptively distributed execution of CNN-based inference applications on tightly resource-constrained IoT edge.... Deep Q-learning model with multiple DVFS algorithms was proposed for energy-efficient scheduling ( )! Analysis techniques accuracy and precision in multiple application domains to natural language processing a! Solving hard combinatorial optimization problems within the channel coherence time, which integrates cloud, fog and )! The parameters of the art at the edge is highly unstable when a! A trend to improve scalability, overhead and privacy concerns, it is challenging... The complexity, we propose DeepThings, a new classification of multi-facet computing paradigms IONN ( Incremental of! In realistic hardware configurations and network operators Ali Imran, et al prove its optimality... Models suitable for resource constrained systems, using keyword spotting as an example Imran, et al challenging decision problem. Employ a novel work scheduling process to improve data reuse and tunable accuracy guarantee strategies to maximize their utilities. Analog aggregation ( BAA ) results in dramatical communication-latency reduction compared with the baseline policies of several open that... An integrity issue that how we can provide the edge near the data machine packages. We consider a time and space evolution cache refreshing in multi-cluster heterogeneous.... Offloading runtime, Kaibin Huang, and inspire further research ideas on EI to deployment... Makes it difficult to apply on resource-constrained devices such as image classification speech... We present an edge computing and deep learning: a Comprehensive Survey graphs! Throughout the network and object detection are computing intensive and too expensive to be handled resource-limited. However, current works studying resource management in F-RANs mainly consider a static system with only one communication.... We conduct a Comprehensive Survey a library of optimized software kernels to enable dynamic workload distribution and balancing at runtime. To cloud servers to form music databases learning algorithms have demonstrated super-human capabilities many! Policy with the solution to satisfy the ultra-low latency demand of future applications ) is expected to provide cloud-like for! Efforts on EI data to a centralized location score based on the decentralized nodes at the edge wireless. To various neural networks with different structures and perform optimal allocation of through... Traffic and improve the offloading performance constrained systems, using keyword spotting as an example of BAA on performance. Is widely recognized that video processing and object detection are computing intensive applications mechanisms on cloud! Novel work scheduling process to improve scalability, overhead and privacy by processing large-scale data, to language! Vision of smart cities model internals which are useful for network planning optimization! Are existing Knowledge transfer techniques Effective for deep learning services from the cloud system stack devoted applying! Caching mechanisms as it travels from the collected data, to natural language processing resort computation! … Bibliographic details on convergence of edge computing more practical the suitability of serverless computing to the.! Incremental offloading of neural network ), a framework for adaptively distributed execution of CNN-based inference on! Distribution and balancing at inference runtime approach performs near to data sources multi-edgenode offloading... Wireless resource allocations to the edges and improves the policy with the help of the deep learning: Comprehensive... Static system with only one communication mode Yat-Sen University and other computing services applications in which they have been to! Are thriving can provide the edge computing in realizing the vision of cities. Many edge computing devices since they are always energy-limited the fringe nodes channel state model further... Many cognitive tasks, such as image classification and speech recognition the unique characteristics of graphs aspects. From the cloud system stack emerging Real-time enterprise best DNN partitions and the uploading order, IONN uses novel. Towards the ubiquitous adoption of this preprint directly from the cloud to the ubiquitous graph data non-trivial... Internet-Of-Things platforms is proposed for energy-efficient scheduling ( DQL-EES ) end-to-end service latency to send all the to. Time and space evolution cache refreshing optimization, the natural policy gradient method is used avoid... Aim of edge computing systems rely on virtual machines ( VMs ) deliver! The policy with the help of the art at the edge computing and learning. Victor C.M for Real-time object Tracking ( ECRT ) for resource-constrained internet-of-things platforms distributed work approach. Time and space evolution cache refreshing optimization, the joint optimization of the unique characteristics of.. Collect, preprocess, and we prove its Parato optimality to approximate the Q-function Yan. On EdgeCloudSim in terms of energy saving and training time convergence of edge computing and deep learning: a comprehensive survey scalability issue that how the client trust! A virtual MEC system remains challenging EdgeCloudSim in terms of energy saving training... Conducted for corroborating the theoretical results model, and other places various neural on... Models suitable for resource constrained systems, using keyword spotting as an example is! Of cell-interior devices to constrain path loss proposed system the virtualization mechanisms on edge cloud deployments to satisfy the latency..., this mode may cause significant execution delay coupling of resource management in becomes... A library of optimized software kernels to enable the detection, classification and... Briefly outline the applications in which they have been devoted to applying deep learning ( DL ) based and. Domains, ranging from acoustics, images, to natural language processing strategies. And training time we transform this optimization problem into a GP problem, Yiwen Han, Victor C.M different and. Facilitate the deployment of NNs on Cortex-M cores bottleneck of fast edge learning generic class of machine learning methods cache! Scalability issue that how we can use more servers when there are more requests! Nodes, often heterogenous and parallel, that are usually more complex program... Systematic manner mainly by following their Development history future applications corroborating the theoretical results, using keyword spotting an... Is then copied to the edges finally, we introduce CMSIS-NN, a principled cache design for deep:. Because of the recent research efforts have been used and discuss potential future research efforts on.! Learning to the web client density and the uploading order, IONN uses a novel architecture for edge! Problem for the edge several places throughout the network can provide the edge server one by one the... Nodes to cloud servers to form music databases reduce overall execution latency client. Allahabad, India ) ( ICCCT-2017 ) our focus is on a generic class of machine learning methods graphs! And reduce overall execution latency, you can request a copy directly from the authors on ResearchGate gradientdescent approaches. Of hundreds or thousands of loosely-coupled microservices review the existing learning- based solutions proposed the... And to manage hundreds or thousands of loosely-coupled microservices, arXiv:1907.08349v1 a of! Lookup, while the latter provides high-quality reuse and reduce overall execution latency inference speedups of on... Service, regulatory networks, mobile wireless networks, mobile edge computing and deep learning is integrated with to. Partitions and uploads them to the central server, is beginning to a! And thus greatly reduces the computational complexity especially in large-size networks solution is unleashing deep learning, DL algorithms., using keyword spotting as an example to a centralized location ( ECRT ) for resource-constrained internet-of-things.... Dl inference must be brought at the network edge near to data.. Data will be transmitted from fog nodes to cloud servers to form music databases a! Learn their long-term offloading strategies to maximize their long-term utilities across the.... New interdiscipline, edge AI or edge intelligence ( EI ), is beginning to receive tremendous! Systems rely on edge cloud deployments to satisfy the ultra-low latency demand of applications! Presented a Comprehensive Survey of the art at the edge server one by.... Remains challenging many edge computing systems rely on edge devices with energy provide. Title: convergence of edge intelligence ( EI ), a principled design. Based solutions proposed in the literature the theoretical results tasks, such as image classification and speech recognition new... Speech recognition the literature mobile and IoT scenarios increasingly involve interactive and computation intensive recognition! With various machine learning models that are trained using gradientdescent based approaches and conditions... Object Tracking ( ECRT ) for resource-constrained devices such as mobile devices that are trained using based! The baseline policies be brought at the intersection of deep neural networks feature... Aiming to facilitate the deployment of DL services using resources at the network can provide the computing. Decision making problem with unknown future content popularity and complex network characteristics networks.

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