Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
暂无分享,去创建一个
Xu Chen | Junshan Zhang | En Li | Ke Luo | Liekang Zeng | Zhi Zhou | Junshan Zhang | Xu Chen | Zhi Zhou | Liekang Zeng | Ke Luo | En Li
[1] Paramvir Bahl,et al. Glimpse: Continuous, Real-Time Object Recognition on Mobile Devices , 2015, SenSys.
[2] Jing Li,et al. IF-CNN: Image-Aware Inference Framework for CNN With the Collaboration of Mobile Devices and Cloud , 2018, IEEE Access.
[3] Katherine Guo,et al. Precog: prefetching for image recognition applications at the edge , 2017, SEC.
[4] Zhenming Liu,et al. DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics , 2018, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.
[5] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] H. T. Kung,et al. Distributed Deep Neural Networks Over the Cloud, the Edge and End Devices , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).
[7] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[8] Stephen P. Boyd,et al. Randomized gossip algorithms , 2006, IEEE Transactions on Information Theory.
[9] Cecilia Mascolo,et al. LEO: scheduling sensor inference algorithms across heterogeneous mobile processors and network resources , 2016, MobiCom.
[10] Randy H. Katz,et al. A Berkeley View of Systems Challenges for AI , 2017, ArXiv.
[11] Jon Crowcroft,et al. Privacy-Preserving Machine Learning Based Data Analytics on Edge Devices , 2018, AIES.
[12] Yong Wang,et al. Low-Latency Broadband Analog Aggregation for Federated Edge Learning , 2018, ArXiv.
[13] Deniz Gündüz,et al. Machine Learning at the Wireless Edge: Distributed Stochastic Gradient Descent Over-the-Air , 2019, 2019 IEEE International Symposium on Information Theory (ISIT).
[14] Nikhil R. Devanur,et al. PipeDream: Fast and Efficient Pipeline Parallel DNN Training , 2018, ArXiv.
[15] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[16] Zhuo Chen,et al. Bandwidth-Efficient Live Video Analytics for Drones Via Edge Computing , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).
[17] Fengyuan Xu,et al. A Privacy-Preserving Deep Learning Approach for Face Recognition with Edge Computing , 2018, HotEdge.
[18] Matthieu Cord,et al. Gossip training for deep learning , 2016, ArXiv.
[19] Vitaly Shmatikov,et al. Privacy-preserving deep learning , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[20] Sergio Guadarrama,et al. Speed/Accuracy Trade-Offs for Modern Convolutional Object Detectors , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Yifan Wang,et al. pCAMP: Performance Comparison of Machine Learning Packages on the Edges , 2019, HotEdge.
[22] William J. Dally,et al. Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training , 2017, ICLR.
[23] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[24] Amar Phanishayee,et al. Accelerating Deep Learning Workloads Through Efficient Multi-Model Execution , 2018 .
[25] Mohammad Alian,et al. A Network-Centric Hardware/Algorithm Co-Design to Accelerate Distributed Training of Deep Neural Networks , 2018, 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[26] Paramvir Bahl,et al. Low Latency Geo-distributed Data Analytics , 2015, SIGCOMM.
[27] Mianxiong Dong,et al. Deep Learning for Smart Industry: Efficient Manufacture Inspection System With Fog Computing , 2018, IEEE Transactions on Industrial Informatics.
[28] Martin Jaggi,et al. Sparsified SGD with Memory , 2018, NeurIPS.
[29] Ming Zhao,et al. Are Existing Knowledge Transfer Techniques Effective for Deep Learning with Edge Devices? , 2018, 2018 IEEE International Conference on Edge Computing (EDGE).
[30] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[31] Xu Chen,et al. Exploiting Massive D2D Collaboration for Energy-Efficient Mobile Edge Computing , 2017, IEEE Wireless Communications.
[32] Xiao Zeng,et al. NestDNN: Resource-Aware Multi-Tenant On-Device Deep Learning for Continuous Mobile Vision , 2018, MobiCom.
[33] Ramesh K. Sitaraman,et al. Optimizing Grouped Aggregation in Geo-Distributed Streaming Analytics , 2015, HPDC.
[34] Diana Marculescu,et al. Designing Adaptive Neural Networks for Energy-Constrained Image Classification , 2018, 2018 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).
[35] Vivienne Sze,et al. Designing Energy-Efficient Convolutional Neural Networks Using Energy-Aware Pruning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[36] Joseph E. Gonzalez,et al. ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Scale , 2018 .
[37] Léon Bottou,et al. Large-Scale Machine Learning with Stochastic Gradient Descent , 2010, COMPSTAT.
[38] Joel Emer,et al. Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks , 2016, CARN.
[39] Zhi Zhou,et al. Boomerang: On-Demand Cooperative Deep Neural Network Inference for Edge Intelligence on the Industrial Internet of Things , 2019, IEEE Network.
[40] Samvit Jain,et al. ReXCam: Resource-Efficient, Cross-Camera Video Analytics at Enterprise Scale , 2018, ArXiv.
[41] Tara Javidi,et al. Peer-to-peer Federated Learning on Graphs , 2019, ArXiv.
[42] Ali Farhadi,et al. You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[43] Mahadev Satyanarayanan,et al. Towards wearable cognitive assistance , 2014, MobiSys.
[44] Trevor N. Mudge,et al. Neurosurgeon: Collaborative Intelligence Between the Cloud and Mobile Edge , 2017, ASPLOS.
[45] Abhinav Vishnu,et al. GossipGraD: Scalable Deep Learning using Gossip Communication based Asynchronous Gradient Descent , 2018, ArXiv.
[46] Carole-Jean Wu,et al. Machine Learning at Facebook: Understanding Inference at the Edge , 2019, 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA).
[47] Mani B. Srivastava,et al. Mitigating Multi-tenant Interference in Continuous Mobile Offloading , 2018, CLOUD.
[48] Weisong Shi,et al. EdgeOS_H: A Home Operating System for Internet of Everything , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).
[49] Hanlin Tang,et al. Communication Compression for Decentralized Training , 2018, NeurIPS.
[50] Gregory R. Ganger,et al. Mainstream: Dynamic Stem-Sharing for Multi-Tenant Video Processing , 2018, USENIX Annual Technical Conference.
[51] Quan Quan,et al. A portable, automatic data qantizer for deep neural networks , 2018, PACT.
[52] Seong-Lyun Kim,et al. Blockchained On-Device Federated Learning , 2018, IEEE Communications Letters.
[53] Katherine Guo,et al. Cachier: Edge-Caching for Recognition Applications , 2017, 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS).
[54] Dan Wang,et al. Data-driven Task Allocation for Multi-task Transfer Learning on the Edge , 2019, 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS).
[55] Paramvir Bahl,et al. Real-Time Video Analytics: The Killer App for Edge Computing , 2017, Computer.
[56] Qun Li,et al. eSGD: Communication Efficient Distributed Deep Learning on the Edge , 2018, HotEdge.
[57] Shih-Chieh Chang,et al. A Dynamic Deep Neural Network Design for Efficient Workload Allocation in Edge Computing , 2017, 2017 IEEE International Conference on Computer Design (ICCD).
[58] Song Guo,et al. Edge Intelligence for the Industrial Internet of Things , 2019, IEEE Netw..
[59] K. B. Letaief,et al. A Survey on Mobile Edge Computing: The Communication Perspective , 2017, IEEE Communications Surveys & Tutorials.
[60] Xu Chen,et al. Edge Intelligence: On-Demand Deep Learning Model Co-Inference with Device-Edge Synergy , 2018, MECOMM@SIGCOMM.
[61] Venkatesh Saligrama,et al. Adaptive Neural Networks for Efficient Inference , 2017, ICML.
[62] Weisong Shi,et al. EdgeOS_H: A Home Operating System for Internet of Everything , 2017, ICDCS 2017.
[63] Soo-Mook Moon,et al. IONN: Incremental Offloading of Neural Network Computations from Mobile Devices to Edge Servers , 2018, SoCC.
[64] Yonggang Wen,et al. JALAD: Joint Accuracy-And Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution , 2018, 2018 IEEE 24th International Conference on Parallel and Distributed Systems (ICPADS).
[65] Gu-Yeon Wei,et al. Minerva: Enabling Low-Power, Highly-Accurate Deep Neural Network Accelerators , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).
[66] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[67] Paramvir Bahl,et al. The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.
[68] Paramvir Bahl,et al. Live Video Analytics at Scale with Approximation and Delay-Tolerance , 2017, NSDI.
[69] Paul J. Werbos,et al. Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.
[70] Vivienne Sze,et al. Efficient Processing of Deep Neural Networks: A Tutorial and Survey , 2017, Proceedings of the IEEE.
[71] Zhiwei Xu,et al. KLRA: A Kernel Level Resource Auditing Tool For IoT Operating System Security , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).
[72] Ion Stoica,et al. Chameleon: scalable adaptation of video analytics , 2018, SIGCOMM.
[73] Peter Richtárik,et al. Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.
[74] Kin K. Leung,et al. Adaptive Federated Learning in Resource Constrained Edge Computing Systems , 2018, IEEE Journal on Selected Areas in Communications.
[75] Alexandros G. Dimakis,et al. Gradient Coding: Avoiding Stragglers in Distributed Learning , 2017, ICML.
[76] Bo Hu,et al. FoggyCache: Cross-Device Approximate Computation Reuse , 2018, MobiCom.
[77] Hui Liu,et al. On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework , 2018, MobiSys.
[78] Philip S. Yu,et al. Not Just Privacy: Improving Performance of Private Deep Learning in Mobile Cloud , 2018, KDD.
[79] Quoc V. Le,et al. Neural Architecture Search with Reinforcement Learning , 2016, ICLR.
[80] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[81] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[82] Nicholas D. Lane,et al. DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware , 2017, MobiSys.
[83] Andreas Gerstlauer,et al. DeepThings: Distributed Adaptive Deep Learning Inference on Resource-Constrained IoT Edge Clusters , 2018, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.
[84] Yu Wang,et al. Towards Real-Time Object Detection on Embedded Systems , 2018, IEEE Transactions on Emerging Topics in Computing.
[85] Dan Wang,et al. Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge , 2019, IEEE INFOCOM 2019 - IEEE Conference on Computer Communications.
[86] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[87] Onur Mutlu,et al. Gaia: Geo-Distributed Machine Learning Approaching LAN Speeds , 2017, NSDI.
[88] Paramvir Bahl,et al. VideoEdge: Processing Camera Streams using Hierarchical Clusters , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).
[89] Srikumar Venugopal,et al. Shadow Puppets: Cloud-level Accurate AI Inference at the Speed and Economy of Edge , 2018, HotEdge.
[90] Jason Weston,et al. Natural Language Processing (Almost) from Scratch , 2011, J. Mach. Learn. Res..
[91] Song Han,et al. AMC: AutoML for Model Compression and Acceleration on Mobile Devices , 2018, ECCV.
[92] Yiran Chen,et al. MeDNN: A distributed mobile system with enhanced partition and deployment for large-scale DNNs , 2017, 2017 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).
[93] Yehia El-khatib,et al. Adaptive deep learning model selection on embedded systems , 2018, LCTES.
[94] Blaise Agüera y Arcas,et al. Communication-Efficient Learning of Deep Networks from Decentralized Data , 2016, AISTATS.
[95] Forrest N. Iandola,et al. How to scale distributed deep learning? , 2016, ArXiv.
[96] Saibal Mukhopadhyay,et al. Edge-Host Partitioning of Deep Neural Networks with Feature Space Encoding for Resource-Constrained Internet-of-Things Platforms , 2018, 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS).
[97] Xu Chen,et al. In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning , 2018, IEEE Network.
[98] Sungroh Yoon,et al. Big/little deep neural network for ultra low power inference , 2015, 2015 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).
[99] Matei Zaharia,et al. NoScope: Optimizing Deep CNN-Based Queries over Video Streams at Scale , 2017, Proc. VLDB Endow..
[100] Daniel Svozil,et al. Introduction to multi-layer feed-forward neural networks , 1997 .
[101] Mehdi Bennis,et al. On-Device Federated Learning via Blockchain and its Latency Analysis , 2018, ArXiv.
[102] Yves Chauvin,et al. Backpropagation: theory, architectures, and applications , 1995 .
[103] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[104] Weisong Shi,et al. Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.
[105] Hamed Haddadi,et al. A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics , 2017, IEEE Internet of Things Journal.
[106] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[107] Takayuki Nishio,et al. Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge , 2018, ICC 2019 - 2019 IEEE International Conference on Communications (ICC).
[108] Steven Bohez,et al. The cascading neural network: building the Internet of Smart Things , 2017, Knowledge and Information Systems.
[109] Nicholas D. Lane,et al. DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
[110] H. T. Kung,et al. BranchyNet: Fast inference via early exiting from deep neural networks , 2016, 2016 23rd International Conference on Pattern Recognition (ICPR).
[111] Xiaoyan Wang,et al. Big Data Privacy Preserving in Multi-Access Edge Computing for Heterogeneous Internet of Things , 2018, IEEE Communications Magazine.
[112] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[113] Dong Yu,et al. Deep Learning: Methods and Applications , 2014, Found. Trends Signal Process..
[114] Chen Zhang,et al. FFS-VA: A Fast Filtering System for Large-scale Video Analytics , 2018, ICPP.
[115] Yiran Chen,et al. MoDNN: Local distributed mobile computing system for Deep Neural Network , 2017, Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017.
[116] Amar Phanishayee,et al. PipeDream: Pipeline Parallelism for DNN Training , 2018 .
[117] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[118] Hyeontaek Lim,et al. Picking Interesting Frames in Streaming Video , 2018 .