Moreover it considers security, power efficiency and adaptiveness as main objectives during the whole development process. 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. Convergence of Edge Computing and Deep Learning: A Comprehensive Survey, preprint, 2019; Research Papers 2020. Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. 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. 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. 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. 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. (SNR)] and the expected update-truncation ratio. In this paper, we proposecross-device approximate computation reuse, which minimizes redundant computation by harnessing the "equivalence'' between different input values and reusing previously computed outputs with high confidence. Using the numerical simulations, we demonstrate the learning capacity of the proposed algorithm and analyze the end-to-end service latency. We also analyze the differences and compositions of different methods. This paper proposes a novel architecture for DNN edge computing based on the blockchain technology. 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. surveillance and autonomous driving. 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. 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. Thus, recently, a better solution is unleashing deep learning services from the cloud to the edge near to data sources. Modern processors include instruction caches to speed up instruction access and memory caches to accelerate data access. 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 work, the effects of BAA on learning performance are quantified targeting a single-cell random network. 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. Therefore, edge intelligence, aiming to facilitate the deployment of deep learning services by edge computing, has received great attention. Embedded Development Boards for Edge-AI: A Comprehensive Report. Authors: Xiaofei Wang, Yiwen Han, Victor C.M. 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. © 2008-2020 ResearchGate GmbH. The aim of edge … Title: Convergence of Edge Computing and Deep Learning: A Comprehensive Survey Authors: Xiaofei Wang , Yiwen Han , Victor C.M. The main advantages are user independence, trans-parency, and new unrated item recommendation, but suffer new-item problems [34].A typical CB recommender system … The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions. This paper proposes a campus edge computing network in the hardware–software co-design process. Preprints and early-stage research may not have been peer reviewed yet. We propose a reinforcement learning (RL) based privacy-aware offloading scheme to help healthcare IoT devices protect both the user location privacy and the usage pattern privacy. Emerging technologies and applications including Internet of Things (IoT), social networking, and crowd-sourcing generate large amounts of data at the network edge. 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. Second, DL inference must be brought at the edge, to overcome limitations posed by the classically-used cloud computing paradigm. This paper simultaneously tackles the issues of content caching strategy, computation offloading policy, and radio resource allocation, and propose a joint optimization solution for the fog-enabled IoT. The former achieves scalable and constant lookup, while the latter provides high-quality reuse and tunable accuracy guarantee. 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. It is proposed that the updates simultaneously transmitted by devices over broadband channels should be analog aggregated “over-the-air” by exploiting the waveform-superposition property of a multi-access channel. Existing optimizations typically resort to computation offloading or simplified on-device processing. To do so, it introduces and discusses: 1) edge … Therefore, edge intelligence, aiming to facilitate the deployment of deep learning services by edge computing, has received great attention. 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. 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. DeepCache benefits model execution efficiency by exploiting temporal locality in input video streams. Then, the system can collect, preprocess, and store raw music data on the fringe nodes. Finally, we briefly outline the applications in which they have been used and discuss potential future research directions. Finally, there is an integrity issue that how the client can trust the result coming from anonymous edge servers. Experiments based on a neural network and a real dataset are conducted for corroborating the theoretical results. We think the blockchain technology can solve these issues to make edge computing more practical. 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). ∙ 41 ∙ share . • The identification of key requirements to envision the Edge computing … Web content moves through many caching mechanisms as it travels from the disk of the origin server to the Web client. To tackle this problem, a deep Q-learning model with multiple DVFS algorithms was proposed for energy-efficient scheduling (DQL-EES). Embedded Development Boards for Edge-AI: A Comprehensive Report. ∙ 0 ∙ share 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. A post-decision state learning method uses the known channel state model to further improve the offloading performance. Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. : Convergence of Recommender Systems and Edge Computing: Comprehensive Survey FIGURE 5. Recently, several machine learning packages based on edge devices have been announced which aim to offload the computing to the edges. ∙ 41 ∙ share . 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. 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. This special issue will bring together academic and industrial researchers to identify and discuss technical challenges and recent results related to the efficient neural network design for convergence of deep learning and edge computing. Numerical results obtained demonstrate the effectiveness of our proposed method, and prove that the energy and delay costs can be significantly reduced by sacrificing the QoR of the offloaded AI tasks. Therefore, recommender systems should be designed sophisticatedly and further customized to fit in the resource-constrained edge … In this survey, we highlight the role of edge computing in realizing the vision of smart cities. Abstract: Many edge computing systems rely on virtual machines (VMs) to deliver their services. Inspired by the depthwise separable convolution and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural Network (L-CNN) is introduced in this paper. 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. 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. 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. To address this issue, this work is focused on designing a low-latency multi-access scheme for edge learning. Since edge nodes’ communication and computing capacities are limited which leads resource contention when many MUs offload to the same edge node at the same time, we formulate this problem as a noncooperative exact potential game (EPG), where each MU, in each time slot, selfishly maximizes its number of processed central processor unit (CPU) cycles and reduces its energy consumption. The use of Deep Learning and Machine Learning is becoming pervasive day by day … Products today are built with machine intelligence as a central attribute, and consumers are beginning to expect near-human interaction with the appliances they use. 09/02/2020 ∙ by Hamza Ali Imran, et al. Bibliographic details on Convergence of Edge Computing and Deep Learning: A Comprehensive Survey. Some features of the site may not work correctly. Mobile edge computing (MEC) is expected to provide cloud-like capacities for mobile users (MUs) at the edge of wireless networks. 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. Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. 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… We divide the existing methods into five categories based on their model architectures and training strategies: graph recurrent neural networks, graph convolutional networks, graph autoencoders, graph reinforcement learning, and graph adversarial methods. The former is generally at the expense of reducing accuracy, and the segmentation of the model has no unified migration tool for the DNN model of different applications. A Survey of Mobile Edge Computing in the Industrial Internet. 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 , little research has been done to evaluate these packages on the edges, making it difficult for end users to select an appropriate pair of software and hardware. 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. Assuming that channel information is static and available to MUs, we show that MUs could achieve a Nash Equilibrium via a best response based offloading mechanism. To this end, we conduct a comprehensive survey of the recent research efforts on EI. The deep neural network (DNN) is employed as the function approximator to estimate the value functions in the critic part due to the extremely large state and action space in our problem. This paper proposes IONN (Incremental Offloading of Neural Network), a partitioning-based DNN offloading technique for edge computing. A Multi-update Deep Reinforcement Learning Algorithm for Edge Computing Service Offloading ... Extensive simulation-based testing shows that the proposed algorithm has fast convergence and improves the system performance more than other three alternative solutions do. This requires quickly solving hard combinatorial optimization problems within the channel coherence time, which is hardly achievable with conventional numerical optimization methods. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. To support next generation services, 5G mobile network architectures are increasingly adopting emerging technlo-gies like software-defined networking (SDN) and network function virtualization (NFV). Trained DNN can provide online response to content placement in a multi-cluster HetNet model instantaneously. 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