Embedded Development Boards for Edge-AI: A Comprehensive Report. In this paper, we present a comprehensive sur… A Handheld Gun Detection Using Faster R-CNN Deep Learning. IONN divides a client's DNN model into a few partitions and uploads them to the edge server one by one. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. In this work, we attempt to evaluate the suitability of serverless computing to run deep learning inferencing tasks. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. Training machine learning model on IoT device is a natural trend due to the growing computation power and the great ability to collect various data of modern IoT device.In this work, we consider an edge based distributed deep learning framework in which many edge devices collaborate to train a model while using an edge server as the parameter server. MEC provides computing and storage service for the edge of network, which enables MUs to execute applications efficiently and meet the delay requirements. We also analyze the differences and compositions of different methods. However, deploying MEC systems faces many challenges, one of which is to achieve an efficient distributed offloading mechanism for multiple users in time-varying wireless environments. Due to the rise of artificial intelligence and machine learning, a small amount of research work has begun to study how to design computing migration and edge caching strategies based on artificial intelligence algorithms in mobile edge computing . One is an availability issue that how we can provide the edge server with some incentives to run the client' apps. In this work, we consider a time and space evolution cache refreshing in multi-cluster heterogeneous networks. 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. We then provide a comprehensive overview of these methods in a systematic manner mainly by following their development history. 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. Furthermore, compared to existing work sharing methods, our distributed work stealing and work scheduling improve throughput by 1.7-2.2x with multiple dynamic data sources. Therefore, the efficient deep neural network design should be deeply investigated on edge computing scenarios. In this paper, we discuss the challenges of deploying neural networks on microcontrollers with limited memory, compute resources and power budgets. In this survey, we highlight the role of edge computing in realizing the vision of smart cities. The framework of a content-based recommender systems. : Convergence of Recommender Systems and Edge Computing: Comprehensive Survey FIGURE 5. The other is a scalability issue that how we can use more servers when there are more DNN requests. Moreover, transfer learning is integrated with DRL to accelerate learning process. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. • A new classification of multi-facet computing paradigms within Edge computing. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey • Due to efficiency and latency issues, the current cloud computing service architecture hinders. 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. However, DQL-EES is highly unstable when using a single stacked auto-encoder to approximate the Q-function. DeepCache benefits model execution efficiency by exploiting temporal locality in input video streams. Request PDF | Convergence of Edge Computing and Deep Learning: A Comprehensive Survey | Ubiquitous sensors and smart devices from factories and communities guarantee massive amounts … Edge computing has emerged as a promising technique because of its advantages in providing low-latency computation offloading services for resource-limited mobile user devices and IoT applications. • The exploration of open research challenges. Further, we present a deep RL based offloading scheme to further accelerate the learning speed. In this article, we provide a comprehensive survey of the latest efforts on the deep-learning-enabled edge computing To this end, we conduct a comprehensive survey of the recent research efforts on EI. In this work, we aim at filling this gap by presenting openLEON, an open source muLti-access Edge cOmputiNg end-to-end emulator that operates from the edge data center to the mobile users. In the … retrieval methods, statistical learning and machine learning methods. Therefore, edge intelligence, aiming to facilitate the deployment of DL services by edge computing, has received significant attention. The experimental study has validated the design of L-CNN and shown it is a promising approach to computing intensive applications at the edge. Simulation results show that this scheme can reduce the computation latency, save the energy consumption, and improve the privacy level of the healthcare IoT device compared with the benchmark scheme. Edge computing, where a fine mesh of compute nodes are placed close to end devices, is a viable way to meet the high computation and low-latency requirements of deep learning on edge devices and also provides additional benefits in terms of privacy, bandwidth efficiency, and scalability. Title: Convergence of Edge Computing and Deep Learning: A Comprehensive Survey Authors: Xiaofei Wang , Yiwen Han , Victor C.M. Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. The server incrementally builds the DNN model as each DNN partition arrives, allowing the client to start offloading partial DNN execution even before the entire DNN model is uploaded. With regard to mutually beneficial edge intelligence and intelligent edge, this paper introduces and discusses: 1) the application scenarios of both; 2) the practical implementation methods and enabling technologies, namely DL training and inference in the customized edge computing framework; 3) challenges and future trends of more pervasive and fine-grained intelligence. $2\%-2.4\%$ However, much of the deep learning revolution has been limited to the cloud. When combined, DeepThings provides scalable CNN inference speedups of 1.7x-3.5x on 2-6 edge devices with less than 23MB memory each. By a simple quantization scheme, we design the learning policy in the Double Deep Q-Network (DDQN) framework, which is shown to have better stability and convergence properties. We provide the performance bound of this scheme regarding the privacy level, the energy consumption and the computation latency for three typical healthcare IoT offloading scenarios. Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. 随着万物互联时代的到来，网络边缘设备产生的数据量快速增加，带来了更高的数据传输带宽需求，同时，新型应用也对数据处理的实时性提出了更高要求，传统云计算模型已经无法有效应对，因此，边缘计算应运而生。 边缘计算的基本理念是将计算任务在接近数据源的计算资源上运行，可以有效减小计算系统的延迟，减少数据传输带宽，缓解云计算中心压力，提高可用性，并能够保护数据安全和隐私。得益于这些优势，边缘计算从2012年以来迅速发展。 近年来，随着万物互联时代的快速到来和无线网络的普及， … Lim et al. We present DeepCache, a principled cache design for deep learning inference in continuous mobile vision. Association for Computing … 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. DeepThings employs a scalable Fused Tile Partitioning (FTP) of convolutional layers to minimize memory footprint while exposing parallelism. Driving by this trend, there is an urgent need to push the AI frontiers to the network edge so as to fully unleash the potential of the edge big data. To this end, we consider a popular privacy-preserving framework, Remember the name, "Convergence of Edge Computing and Deep Learning: A Comprehensive Survey", it must be a great success! In this paper, we make a performance comparison of several state-of-the-art machine learning packages on the edges, including TensorFlow, Caffe2, MXNet, PyTorch, and TensorFlow Lite. More specifically, this scheme enables a healthcare IoT device to choose the offloading rate that improves the computation performance, protects user privacy and saves the energy of the IoT device without being aware of the privacy leakage, IoT energy consumption and edge computation model. In this case, we adopt an unknown payoff game framework and prove that the EPG properties still hold. 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. However, due to efficiency and latency issues, the current cloud computing service architecture hinders the vision of “providing artificial intelligence for every person and every organization at everywhere”. Mobile edge caching is a promising technique to reduce network traffic and improve the quality of experience of mobile users. 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… 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. The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data. 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. Different Internet of Things (IoT) applications demand different levels of intelligence and efficiency in processing data. Edge intelligence refers to a set of connected systems and devices for data collection, caching, processing, and analysis in locations close to where data is captured based on artificial intelligence. Consider the scheduling of cell-interior devices to constrain path loss. A Survey of Mobile Edge Computing in the Industrial Internet. At the initial complete cache refreshing optimization, the joint optimization of the activated base station density and the content placement probability is considered. Notably, DeepCache eschews applying video heuristics to model internals which are not pixels but high-dimensional, difficult-to-interpret data. In this paper, we introduce the engineering and research trends of achieving efficient VM management in edge computing. 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. Fog radio access networks (F-RANs) are seen as potential architectures to support services of Internet of Things by leveraging edge caching and edge computing. We further incorporate approximate reuse as a service, called \name, in the computation offloading runtime. Machine learning has changed the computing paradigm. A post-decision state learning method uses the known channel state model to further improve the offloading performance. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions. Thus, unleashing DL services using resources at the network edge near the data sources has emerged as a desirable solution. We believe that by consolidating information scattered across the communication, networking, and DL areas, this survey can help readers to understand the connections between enabling technologies while promoting further discussions on the fusion of edge intelligence and intelligent edge, i.e., Edge DL. 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. We believe that this survey can help readers to garner information scattered across the communication, networking, and deep learning, understand the connections between enabling technologies, and promotes further discussions on the fusion of edge intelligence and intelligent edge. When the number of working nodes increases from 1 to 5, this method can speed up DNN 2-2.5 times, and shows a good acceleration effect. Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. Recently, substantial research efforts have been devoted to applying deep learning methods to graphs, resulting in beneficial advances in graph analysis techniques. DOI: 10.1109/COMST.2020.2970550 Corpus ID: 197935335. We propose a tide ebb algorithm to solve the MASM optimization model, and we prove its Parato optimality. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey • Due to efficiency and latency issues, the current cloud computing service architecture hinders. We also discuss the unique features in the application of DRL in mobile edge caching, and illustrate an example of DRL-based mobile edge caching with trace-data-driven simulation results. The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. This paper proposes a novel architecture for DNN edge computing based on the blockchain technology. And we transform this optimization problem into a GP problem. Bibliographic details on Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. Modern processors include instruction caches to speed up instruction access and memory caches to accelerate data access. 我们在行业顶级刊物IEEE COMST上录取了一篇史诗级关于边缘智能的综述文 … The key feature of our system is that it intelligently partitions compute-intensive tasks such as inferencing a convolutional neural network(CNN) into two parts, which are executed locally on an IoT device and/or on the edge server. In this survey, we comprehensively review the different types of deep learning methods on graphs. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey 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 … A comprehensive survey on all aspects of Edge computing (Cloudlet, Fog and Mobile-Edge). Finally, we explore the tail at scale effects of microservices in real deployments with hundreds of users, and highlight the increased pressure they put on performance predictability. First, we analyze the evolution of edge computing paradigms. © 2008-2020 ResearchGate GmbH. 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 … Convergence of Edge Computing and Deep Learning: A Comprehensive Survey, preprint, 2019; Research Papers 2020. The framework of a content-based recommender systems. 09/02/2020 ∙ by Hamza Ali Imran, et al. However, this mode may cause significant execution delay. ... Changsheng You, Jun Zhang, Kaibin Huang, and Khaled B. Letaief. Abstract: 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 … We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best trade-off between local update and global parameter aggregation to minimize the loss function under a given resource budget. Recently, several machine learning packages based on edge devices have been announced which aim to offload the computing to the edges. FedPerf: A Practitioners’ Guide to Performance of Federated Learning Algorithms, preprint; WAFFLe: Weight Anonymized Factorization for Federated Learning, preprint; Fed+: A Family of Fusion Algorithms for Federated Learning… To leverage rich data distributed at the network edge, a new machine-learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing intelligent services to mobile users. This gives rise to the other tradeoff between the receive SNR and fraction of data exploited in learning. Authors: Xiaofei Wang, Yiwen Han, Victor C.M. The content is stored on the server disk. 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. presented a comprehensive survey of federated learning for mobile edge networks . 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. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. We first examine the key issues in mobile edge caching and review the existing learning- based solutions proposed in the literature. Therefore, a music cognition system is introduced to cognate music and automatically write score based on machine learning methods. 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. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey, preprint, 2019; Research Papers 2020. Such In this paper, we consider a wireless powered MEC network that adopts a binary offloading policy, so that each computation task of wireless devices (WDs) is either executed locally or fully offloaded to an MEC server. In this work, the effects of BAA on learning performance are quantified targeting a single-cell random network. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. This incredibly rapid adoption of Internet of Things (IoT) and e-learning technology, a smart campus provides many innovative applications, such as ubiquitous learning, smart energy, and security services to campus users via numerous IoT devices. Each user equipment (UE) can operate either in cloud RAN (C-RAN) mode or in device-to-device mode, and the resource managed includes both radio resource and computing resource. Caches located in several places throughout the network can provide a variety of benefits to content consumers, content producers, and network operators. 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. Extensive evaluation shows that, when given 95% accuracy target, \name\ consistently harnesses over 90% of reuse opportunities, which translates to reduced computation latency and energy consumption by a factor of 3 to 10. • A better solution is unleashing deep learning services from the cloud to the edge … Unfortunately, this centralized, cloud-based DNN offloading is not appropriate for emerging decentralized cloud infrastructures (e.g., cloudlet, edge/fog servers), where the client may send computation requests to any nearby server located at the edge of the network. Then, the system can collect, preprocess, and store raw music data on the fringe nodes. Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. We first present DeathStarBench, a novel, open-source benchmark suite built with microservices that is representative of large end-to-end services, modular and extensible. (FEEL), where a global AI-model at an edge-server is updated by aggregating (averaging) local models trained at edge devices. Although recent deep learning systems can achieve satisfactory tracking performance, they incur significant compute overhead, which prevents them from wide adoption on resource-constrained IoT platforms. Meanwhile, there are some new problems to decrease the accuracy, such as the potential leakage of user privacy and mobility of user data. ResearchGate has not been able to resolve any citations for this publication. Caching Techniques for Web Content, Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks, Broadband Analog Aggregation for Low-Latency Federated Edge Learning, A Fog Robotic System for Dynamic Visual Servoing, Federated Learning Based on Over-the-Air Computation, Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN, Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing, Self-Driving Car Meets Multi-Access Edge Computing for Deep Learning-Based Caching, An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud & Edge Systems, Federated Learning for Ultra-Reliable Low-Latency V2V Communications, MASM: A Multiple-Algorithm Service Model for Energy-Delay Optimization in Edge Artificial Intelligence, A Blockchain-Enabled Trustless Crowd-Intelligence Ecosystem on Mobile Edge Computing, Multi-tier computing networks for intelligent IoT, ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices, CAVBench: A Benchmark Suite for Connected and Autonomous Vehicles, Smart Surveillance as an Edge Network Service: From Harr-Cascade, SVM to a Lightweight CNN, Blockchain-based edge computing for deep neural network applications, ALOHA: an architectural-aware framework for deep learning at the edge, Joint Optimization of Caching, Computing, and Radio Resources for Fog-Enabled IoT Using Natural Actor-Critic Deep Reinforcement Learning, Deep Reinforcement Learning based Resource Allocation in Low Latency Edge Computing Networks, DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters, DeepCache: Principled Cache for Mobile Deep Vision, FoggyCache: Cross-Device Approximate Computation Reuse, Campus Edge Computing Network Based on IoT Street Lighting Nodes, Bringing Deep Learning at The Edge of Information-Centric Internet of Things, A Locally Distributed Mobile Computing Framework for DNN based Android Applications, IONN: Incremental Offloading of Neural Network Computations from Mobile Devices to Edge Servers. 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