OpenEI: An Open Framework for Edge Intelligence

Zhang, Xingzhou; Wang, Yifan; Lu, Sidi; Liu, Liangkai; Xu, Lanyu; Shi, Weisong

doi:10.1109/icdcs.2019.00182

Cited by 87 publications

(34 citation statements)

References 52 publications

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“…They have first performed data aggregation using federated learning and then created personalized models for each edge using transfer learning. OpenEI [82], an edge intelligence framework that was proposed by Xingzhou Zhang et.al. This framework with lightweight software equips with the edges as well as intelligent computing and data sharing capability.…”

Section: Review Of Related State-of-the-artmentioning

confidence: 99%

A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

Sufian

Ghosh

Sadiq

et al. 2020

Journal of Systems Architecture

117

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Global Health sometimes faces pandemics as are currently facing COVID-19 disease. The spreading and infection factors of this disease are very high. A huge number of people from most of the countries are infected within six months from its first report of appearance and it keeps spreading. The required systems are not ready up to some stages for any pandemic; therefore, mitigation with existing capacity becomes necessary. On the other hand, modern-era largely depends on Artificial Intelligence(AI) including Data Science; Deep Learning(DL) is one of the current flag-bearer of these techniques. It could use to mitigate COVID-19 like pandemics in terms of stop spread, diagnosis of the disease, drug & vaccine discovery, treatment, and many more. But this DL requires large datasets as well as powerful computing resources. A shortage of reliable datasets of a running pandemic is a common phenomenon. So, Deep Transfer Learning(DTL) would be effective as it learns from one task and could work on another task. In addition, Edge Devices(ED) such as IoT, Webcam, Drone, Intelligent Medical Equipment, Robot, etc. are very useful in a pandemic situation. These types of equipment make the infrastructures sophisticated and automated which helps to cope with an outbreak. But these are equipped with low computing resources, so, applying DL is also a bit challenging; therefore, DTL also would be effective there. This article scholarly studies the potentiality and challenges of these issues. It has described relevant technical backgrounds and reviews of the related recent state-of-the-art. This article also draws a pipeline of DTL over Edge Computing as a future scope to assist the mitigation of any pandemic.

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Section: Review Of Related State-of-the-artmentioning

confidence: 99%

A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

Sufian

Ghosh

Sadiq

et al. 2020

Journal of Systems Architecture

117

View full text Add to dashboard Cite

show abstract

“…They have first performed data aggregation using federated learning and then created personalized models for each edge using transfer learning. OpenEI [81], an edge intelligence framework that was proposed by Xingzhou Zhang et.al. This framework with lightweight software equips with the edges as well as intelligent computing and data sharing capability.…”

Section: Review Of Related State-of-the-artmentioning

confidence: 99%

A Survey on Deep Transfer Learning and Edge Computing for Mitigating the COVID-19

Sufian¹,

Ghosh²,

Sadiq³

et al. 2020

Preprint

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“…A large portion of jobs executed by robots are related to computer vision, which exploit cameras and machine learning models to make the robots more intelligent and autonomous [51]. While some related studies focus mainly on performance [28,29,44] without considering the energy consumption, other solutions focus on pruning and compression techniques to make the trained model more energy efficient [40,48].…”

Section: Related Workmentioning

confidence: 99%

E2m

Liu

Chen

Brocanelli

et al. 2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

Self Cite

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Autonomous mobile robots (AMRs) have been widely utilized in industry to execute various on-board computer-vision applications including autonomous guidance, security patrol, object detection, and face recognition. Most of the applications executed by an AMR involve the analysis of camera images through trained machine learning models. Many research studies on machine learning focus either on performance without considering energy efficiency or on techniques such as pruning and compression to make the model more energy-efficient. However, most previous work do not study the root causes of energy inefficiency for the execution of those applications on AMRs. The computing stack on an AMR accounts for 33% of the total energy consumption and can thus highly impact the battery life of the robot. Because recharging an AMR may disrupt the application execution, it is important to efficiently utilize the available energy for maximized battery life. In this paper, we first analyze the breakdown of power dissipation for the execution of computer-vision applications on AMRs and discover three main root causes of energy inefficiency: uncoordinated access to sensor data, performance-oriented model inference execution, and uncoordinated execution of concurrent jobs. In order to fix these three inefficiencies, we propose E2M, an energy-efficient middleware software stack for autonomous mobile robots. First, E2M regulates the access of different processes to sensor data, e.g., camera frames, so that the amount of data actually captured by concurrently executing jobs can be minimized. Second, based on a predefined per-process performance metric (e.g., safety, accuracy) and desired target, E2M manipulates the process execution period to find the best energy-performance trade off. Third, E2M coordinates the execution of the concurrent processes to maximize the total contiguous sleep time of the computing hardware for maximized energy savings. We have implemented a prototype of E2M on HydraOne, a real-world AMR. Our experimental results show that, compared to several baselines, E2M leads to 24% energy savings for the computing platform, which translates into an extra 11.5% of battery time and 14 extra minutes of robot runtime, with a

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OpenEI: An Open Framework for Edge Intelligence

Cited by 87 publications

References 52 publications

A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

A Survey on Deep Transfer Learning to Edge Computing for Mitigating the COVID-19 Pandemic

A Survey on Deep Transfer Learning and Edge Computing for Mitigating the COVID-19

E2m

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