Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing

Zhou, Zhi; Xu, Chunxiao; Li, En; Zeng, Liekang; Luo, Ke; Zhang, Junshan

doi:10.48550/arxiv.1905.10083

Cited by 16 publications

(14 citation statements)

References 74 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Discussions on the topic of mobile DNN computation have recently obtained growing attention. By hosting artificial intel-ligence on mobile devices, mobile DNN computation deploys DNN models close to users in order to achieve more flexible execution as well as more secure interaction [15]. However, it is challenging to directly execute the computation-intensive DNNs on mobile devices due to the limited computation resource.…”

Section: Related Workmentioning

confidence: 99%

“…The principal idea of edge computing [6]- [8] is to sink the cloud computing capability from the network core to the network edges (e.g., base stations and WLAN) in close proximity to end devices [9]- [14]. This novel feature enables computation-intensive and latency-critical DNN-based applications to be executed in a real-time responsive manner (i.e., edge intelligence) [15]. By leveraging edge computing, we can design an on-demand lowlatency DNNs inference framework for supporting real-time edge AI applications.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Zeng

Zhou

et al. 2020

IEEE Trans. Wireless Commun.

Self Cite

574

218

View full text Add to dashboard Cite

As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challenging to run computation-intensive DNN-based tasks on mobile devices due to the limited computation resources. What's worse, traditional cloud-assisted DNN inference is heavily hindered by the significant wide-area network latency, leading to poor real-time performance as well as low quality of user experience. To address these challenges, in this paper, we propose Edgent, a framework that leverages edge computing for DNN collaborative inference through device-edge synergy. Edgent exploits two design knobs: (1) DNN partitioning that adaptively partitions computation between device and edge for purpose of coordinating the powerful cloud resource and the proximal edge resource for real-time DNN inference; (2) DNN right-sizing that further reduces computing latency via early exiting inference at an appropriate intermediate DNN layer. In addition, considering the potential network fluctuation in realworld deployment, Edgent is properly design to specialize for both static and dynamic network environment. Specifically, in a static environment where the bandwidth changes slowly, Edgent derives the best configurations with the assist of regressionbased prediction models, while in a dynamic environment where the bandwidth varies dramatically, Edgent generates the best execution plan through the online change point detection algorithm that maps the current bandwidth state to the optimal configuration. We implement Edgent prototype based on the Raspberry Pi and the desktop PC and the extensive experimental evaluations demonstrate Edgent's effectiveness in enabling ondemand low-latency edge intelligence.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Zeng

Zhou

et al. 2020

IEEE Trans. Wireless Commun.

Self Cite

574

218

View full text Add to dashboard Cite

show abstract

“…This is achieved by placing computer servers at the base stations (BSs) or radio access points [28], [29], [30], [31], [36]. Edge caching and computing platforms further enable edge AI, which trains and deploys powerful machine learning models at the edge servers and mobile devices, and has been regarded as a key supporting technology for IoT [37], [38], [39], [40]. Edge AI is changing the landscape of the semiconductor industry.…”

Section: B Living On the Edgementioning

confidence: 99%

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

View full text Add to dashboard Cite

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

show abstract

“…In recent years, we are experiencing a great surge of Edge Intelligence (see in [4]- [6]). Numerous attempts have been made to combine AI techniques and edge, tapping the profound potential of the ubiquitous deployed edge devices.…”

Section: Related Workmentioning

confidence: 99%

“…Here p is an all-1 10-dimension vector 6 and γ 1 is a concentration parameter controlling the extent of identicalness among clients, say, with γ 1 → 0 each client holds only one class chosen at random (i.e. high degree of non-iid), conversely, all clients have identical access to all classes (i.e.…”

Section: Setupmentioning

confidence: 99%

An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee

Huang

Lin

et al. 2020

IEEE Trans. Parallel Distrib. Syst.

135

View full text Add to dashboard Cite

The issue of potential privacy leakage during centralized AI's model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new paradigm to cope with the privacy issue by allowing clients to perform model training locally, without the necessity to upload their personal sensitive data. In FL, the number of clients could be sufficiently large, but the bandwidth available for model distribution and re-upload is quite limited, making it sensible to only involve part of the volunteers to participate in the training process. The client selection policy is critical to an FL process in terms of training efficiency, the final model's quality as well as fairness. In this paper, we will model the fairness guaranteed client selection as a Lyapunov optimization problem and then a C 2 MAB-based method is proposed for estimation of the model exchange time between each client and the server, based on which we design a fairness guaranteed algorithm termed RBCS-F for problem-solving. The regret of RBCS-F is strictly bounded by a finite constant, justifying its theoretical feasibility. Barring the theoretical results, more empirical data can be derived from our real training experiments on public datasets.

show abstract

Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing

Cited by 16 publications

References 74 publications

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Mobile Edge Intelligence and Computing for the Internet of Vehicles

An Efficiency-boosting Client Selection Scheme for Federated Learning with Fairness Guarantee

Contact Info

Product

Resources

About