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Edge computing has been widely recognized as a promising solution to support computation-intensive artificial intelligence applications in resource-constrained environments

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

Proceedings of the IEEE, no. 8 (2019): 1738-1762

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摘要

With the breakthroughs in deep learning, the recent years have witnessed a booming of artificial intelligence (AI) applications and services, spanning from personal assistant to recommendation systems to video/audio surveillance. More recently, with the proliferation of mobile computing and Internet of Things (IoT), billions of mobile and...更多

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重点内容
  • W E are living in an unprecedented booming era of artificial intelligence (AI)
  • Looking back to the above evolution of edge computing, it can be foreseen that novel AI applications emerged from the sectors such as industrial IoT, intelligent robots, smart cities and smart home will play a crucial role in the popularization of edge computing
  • The analysis shows that this scheme converges at the same rate as vanilla Stochastic Gradient Descent (SGD) when equipped with error compensation
  • Considering the application scenario that the same application runs on multiple devices in close proximity and the deep neural network (DNN) model often processes similar input data, FoggyCache [95] are proposed to minimize these redundant computations
  • Edge computing has been widely recognized as a promising solution to support computation-intensive AI applications in resource-constrained environments
  • We provide an overview of the overarching architectures, frameworks and emerging key technologies for deep learning model towards training and inference at the network edge
表格
  • Table1: Technologies for distributed DNN training at the edge
  • Table2: A Overview of Systems and Frameworks on EI Model Training
  • Table3: An Overview of Systems and Frameworks on EI Model Infernce
  • Table4: Technologies for distributed DNN inference at the edge
Download tables as Excel
基金
  • ResNet [20] , the state-of-the-art effort, uses the ”shortcut” structure to reach a human-level accuracy with a top-5 error rate below 5%
  • As forecasted by Gartner [33], more than 80 percent of enterprise IoT projects will include an AI component by 2022, up from only 10 percent today
  • • Latency increase up to 1.5% to achieve the optimal block generation than the simulated minimum latency
  • • Reach 91.2%, 86.7%, 81.5% accuracy on MNIST data set with gradient drop ratio 50%, 75%, 87.5% respectively
  • ReXCam reduces computation workload by 4.6x and improves DNN model inference accuracy by 27%
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  • Liekang Zeng received the B.S. degree in computer science from the School of Data and Computer Science, Sun Yat-sen University (SYSU), Guangzhou, China in 2018. He is currently pursuing the masters degree with the School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China. His research interests include mobile edge computing, deep learning, distributed computing.
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  • Zhi Zhou received the B.S., M.E. and Ph.D. degrees in 2012, 2014 and 2017, respectively, all from the School of Computer Science and Technology, Huazhong University of Science and Technology (HUST), Wuhan, China. He is currently a research fellow in School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China. In 2016, he has been a Visiting Scholar at University of Gottingen. He was the sole recipient of 2018 ACM Wuhan & Hubei Computer Society Doctoral Dissertation Award, a recipient of the Best Paper Award of IEEE UIC 2018, and a general co-chair of 2018 International Workshop on Intelligent Cloud Computing and Networking (ICCN). His research interests include edge computing, cloud computing and distributed systems.
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