Techology trend of edge AI

Lee, Yen-Lin; Tsung, Pei-Kuei; Wu, Max

doi:10.1109/vlsi-dat.2018.8373244

Cited by 80 publications

(45 citation statements)

References 3 publications

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“…Since the execution of ML algorithms on IoT devices-near the source of (sensor) data-provides notable advantages, such as lowering the network load (thanks to a reduced amount of data forwarded to the Cloud to be processed) and the latency, a hot IoT trend is to move the intelligence (i.e., execution of Artificial Intelligence, AI, algorithms) from the Cloud to the Edge [8]. Since IoT devices often have significantly lower memory, computational, and energy resources than Cloud platforms, at-the-Edge algorithms have thus to be carefully designed (e.g., ANN models with reduced number of parameters) [9].…”

Section: Introductionmentioning

confidence: 99%

AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting

Codeluppi

Cilfone

Davoli

et al. 2020

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

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Section: Introductionmentioning

confidence: 99%

AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting

Codeluppi

Cilfone

Davoli

et al. 2020

2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

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“…However, smart wearable devices, which support on-line applications alongside the Edge AI, have to protect their private information [37]. Therefore, enabling dedicated security is paramount of importance in future wearable systems [4,25]. While the problems of security and privacy [11,55], power and computational efficiency [22,30,44] in Edge AI have already been of interest in recent studies, research in this area is ongoing.…”

Section: Related Challengesmentioning

confidence: 99%

When wearable technology meets computing in future networks

Ometov

Chukhno

et al. 2021

Proceedings of the 18th ACM International Conference on Computing Frontiers

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Rapid technology advancement, economic growth, and industrialization have paved the way for developing a new niche of small body-worn personal devices, gathered together under a wearabletechnology title. The triggers stimulated by end-users interest have introduced the first generation of mass-consumer wearables in just the past decade. Evidently, the trailblazing ones were not designed with strict energy-consumption restrictions in mind. Thus, wearable-computing-related research remained fragmented. Advanced and sophisticated batteries and communication technologies could be already procurable on devices. Additional solutions for efficient utilization of processing power are still a white spot on the wearable technology roadmap. A-WEAR EU project aims to enhance the understanding of how the superimposition of those technologies would improve wearable devices' energy efficiency, with the research area being far from saturation. We foresee enormous room for research as the Edge computing paradigm is emerging towards hand-held devices. CCS CONCEPTS• General and reference → Cross-computing tools and techniques; • Hardware → Networking hardware; Wireless devices; Emerging architectures; • Computer systems organization → Distributed architectures; • Networks → Network topology types; Wireless access networks.

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“…Also, the industry has shown notable interest in Edge AI. MediaTek has prepared an SW/HW solution for optimizing Edge AI performance [314]. According to the authors, AI on the edge devices takes the advantages of rapid response with low latency, high privacy, better robustness, and more efficient use of network bandwidth.…”

Section: B Ml-based Optimization Of the Edge Infrastructurementioning

confidence: 99%

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

et al. 2020

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The growing network density and unprecedented increase in network traffic, caused by the massively expanding number of connected devices and online services, require intelligent network operations. Machine Learning (ML) has been applied in this regard in different types of networks and networking technologies to meet the requirements of future communicating devices and services. In this article, we provide a detailed account of current research on the application of ML in communication networks and shed light on future research challenges. Research on the application of ML in communication networks is described in: i) the three layers, i.e., physical, access, and network layers; and ii) novel computing and networking concepts such as Multi-access Edge Computing (MEC), Software Defined Networking (SDN), Network Functions Virtualization (NFV), and a brief overview of ML-based network security. Important future research challenges are identified and presented to help stir further research in key areas in this direction. INDEX TERMS Communication networks, machine learning, physical layer, MAC layer, network layer, SDN, NFV, MEC, security, artificial intelligence (AI) I. INTRODUCTION T HE security, availability and performance demands of new applications, services and devices are increasing at a pace higher than anticipated. Real-time responsiveness in application areas like e-health, traffic, and industry requires communication networks to make real-time decisions autonomously. Such real-time autonomous decision-making requires that the network must react and learn from the environment, and control itself without human interventions. However, communication networks have until now taken a different path. Traditional networks rely on human involvement to respond manually to changes such as traffic variation, updates in network functions and services, security breaches, and faults. Human-machine interactions have resulted in network downtime [1], have opened the network to security vulnerabilities [2], and lead to many other challenges in current communication networks [3], [4]. The requirement for human interaction or manual configuration constitutes a major hindrance for a network to use its past experiences to adapt to changing requirements. The general idea is to predict the (future) behavior of a service, network segment, user or User Equipment (UE), and tune the network at run-time based on this information. For instance, the movement trajectory of a user can be predicted using

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Techology trend of edge AI

Cited by 80 publications

References 3 publications

AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting

AI at the Edge: a Smart Gateway for Greenhouse Air Temperature Forecasting

When wearable technology meets computing in future networks

Machine Learning Meets Communication Networks: Current Trends and Future Challenges

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