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Deploying deep learning (DL) on mobile devices has been a notable trend in recent years.
Improving the speed of neural networks on cpus
Vincent Vanhoucke, Andrew Senior, and Mark Z Mao · 2011
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A study of user data integrity during acquisition of android devices
Dohyun Kim · 2013
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Densenet: Implementing efficient convnet descriptor pyramids
Forrest Iandola, Matt Moskewicz, Sergey Karayev, Ross Girshick, Trevor Darrell, and Kurt Keutzer · 2014
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Opencl framework for arm processors with neon support
Gangwon Jo, Won Jong Jeon, Wookeun Jung, Gordon Taft, and Jaejin Lee · 2014
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imashup: a mashup-based framework for service composition
Xuanzhe Liu, Gang Huang, Qi Zhao, Hong Mei, and M Brian Blake · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Deep learning with limited numerical precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan, and Pritish Narayanan · 2015
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Tensorflow: A system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al · 2016
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Fathom: Reference workloads for modern deep learning methods
Robert Adolf, Saketh Rama, Brandon Reagen, Gu-Yeon Wei, and David Brooks · 2016
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Squeezenet: Alexnet-level accuracy with 50x fewer parameters and< 0.5 mb model size
Forrest N Iandola, Song Han, Matthew W Moskewicz, Khalid Ashraf, William J Dally, and Kurt Keutzer · 2016
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Ssd: Single shot multibox detector
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C Berg · 2016
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Vulkan programming guide: The official guide to learning vulkan
Graham Sellers and John Kessenich · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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https://developer.qualcomm.com/sites/default/files/docs/snpe/overview.html , 2017
Snapdragon snpe deep learning framework · 2017
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https://github.com/XiaoMi/mace , 2017
Xiaomi mace deep learning framework · 2017
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Boat: Building auto-tuners with structured bayesian optimization
Valentin Dalibard, Michael Schaarschmidt, and Eiko Yoneki · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Andrew G Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Neurosurgeon: Collaborative intelligence between the cloud and mobile edge
Yiping Kang, Johann Hauswald, Cao Gao, Austin Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, and Alexander A Alemi · 2017
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https://github.com/Tencent/ncnn , 2018
Tencent ncnn deep learning framework · 2018
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Tvm: end-to-end optimization stack for deep learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Haichen Shen, Eddie Q Yan, Leyuan Wang, Yuwei Hu, Luis Ceze, Carlos Guestrin, and Arvind Krishnamurthy · 2018
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Ai benchmark: Running deep neural networks on android smartphones
Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, and Luc Van Gool · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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On-demand deep model compression for mobile devices: A usage-driven model selection framework
Sicong Liu, Yingyan Lin, Zimu Zhou, Kaiming Nan, Hui Liu, and Junzhao Du · 2018
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A new era for web ar with mobile edge computing
Xiuquan Qiao, Pei Ren, Schahram Dustdar, and Junliang Chen · 2018
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Yolov3: An incremental improvement
Joseph Redmon and Ali Farhadi · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Deeptype: On-device deep learning for input personalization service with minimal privacy concern
Mengwei Xu, Feng Qian, Qiaozhu Mei, Kang Huang, and Xuanzhe Liu · 2018
Cited alongside, same era.
Deepcache: Principled cache for mobile deep vision
Mengwei Xu, Mengze Zhu, Yunxin Liu, Felix Xiaozhu Lin, and Xuanzhe Liu · 2018
Cited alongside, same era.
Learning transferable architectures for scalable image recognition
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le · 2018
Cited alongside, same era.
https://github.com/alibaba/MNN , 2019
Alibaba mnn deep learning framework · 2019
Cited alongside, same era.
https://pytorch.org/mobile/home/ , 2019
Pytorch mobile · 2019
Cited alongside, same era.
Embench: Quantifying performance variations of deep neural networks across modern commodity devices
Filter response normalization layer: Eliminating batch dependence in the training of deep neural networks
Saurabh Singh and Shankar Krishnan · 2020
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Understanding and detecting fragmentation-induced compatibility issues for android apps
Lili Wei, Yepang Liu, Shing-Chi Cheung, Huaxun Huang, Xuan Lu, and Xuanzhe Liu · 2020
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Emo: Real-time emotion recognition from single-eye images for resource-constrained eyewear devices
Hao Wu, Jinghao Feng, Xuejin Tian, Edward Sun, Yunxin Liu, Bo Dong, Fengyuan Xu, and Sheng Zhong · 2020
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Cluecorpus2020: A large-scale chinese corpus for pre-training language model
Liang Xu, Xuanwei Zhang, and Qianqian Dong · 2020
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Deepwear: Adaptive local offloading for on-wearable deep learning
Mengwei Xu, Feng Qian, Mengze Zhu, Feifan Huang, Saumay Pushp, and Xuanzhe Liu · 2020
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Mario Almeida, Stefanos Laskaridis, Ilias Leontiadis, Stylianos I Venieris, and Nicholas D Lane · 2019
Cited alongside, same era.
Depthwise convolution is all you need for learning multiple visual domains
Yunhui Guo, Yandong Li, Liqiang Wang, and Tajana Rosing · 2019
Cited alongside, same era.
Ai benchmark: All about deep learning on smartphones in 2019
Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, and Luc Van Gool · 2019
Cited alongside, same era.
μ \mu layer: Low latency on-device inference using cooperative single-layer acceleration and processor-friendly quantization
Youngsok Kim, Joonsung Kim, Dongju Chae, Daehyun Kim, and Jangwoo Kim · 2019
Cited alongside, same era.
Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, et al · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
Cited alongside, same era.
Mnasnet: Platform-aware neural architecture search for mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, and Quoc V Le · 2019
Cited alongside, same era.
Approximate query service on autonomous iot cameras
Mengwei Xu, Xiwen Zhang, Yunxin Liu, Gang Huang, Xuanzhe Liu, and Felix Xiaozhu Lin · 2020
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A subsequent words recommendation scheme for chinese input method based on deep reinforcement learning
Jingyun Yang, Hengjun Wang, and Kexiang Guo · 2020
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Nemo: enabling neural-enhanced video streaming on commodity mobile devices
Hyunho Yeo, Chan Ju Chong, Youngmok Jung, Juncheol Ye, and Dongsu Han · 2020
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Faster-yolo: An accurate and faster object detection method
Yunhua Yin, Huifang Li, and Wei Fu · 2020
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Mobipose: Real-time multi-person pose estimation on mobile devices
Jinrui Zhang, Deyu Zhang, Xiaohui Xu, Fucheng Jia, Yunxin Liu, Xuanzhe Liu, Ju Ren, and Yaoxue Zhang · 2020
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https://github.com/AIIABenchmark/AIIA-DNN-benchmark , 2021
Aiia dnn benchmark · 2021
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https://www.grandviewresearch.com/industry-analysis/artificial-intelligence-ai-market , 2021
Artificial intelligence market analysis report · 2021
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https://dawn.cs.stanford.edu/benchmark/CIFAR10/inference.html , 2021
Dawnbench: An end-to-end deep learning benchmark and competition · 2021
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https://github.com/baidu-research/DeepBench , 2021
Deepbench: Benchmarking deep learning operations on different hardware · 2021
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https://en.wikipedia.org/wiki/Qualcomm_Hexagon , 2021
Qualcomm hexagon · 2021
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Smart at what cost? characterising mobile deep neural networks in the wild
Mario Almeida, Stefanos Laskaridis, Abhinav Mehrotra, Lukasz Dudziak, Ilias Leontiadis, and Nicholas D Lane · 2021
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Towards ubiquitous learning: A first measurement of on-device training performance
Dongqi Cai, Qipeng Wang, Yuanqiang Liu, Yunxin Liu, Shangguang Wang, and Mengwei Xu · 2021
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Workload-and user-aware battery lifetime management for mobile socs
Sofiane Chetoui and Sherief Reda · 2021
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Adaptive inference through early-exit networks: Design, challenges and directions
Stefanos Laskaridis, Alexandros Kouris, and Nicholas D Lane · 2021
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It’s always personal: Using early exits for efficient on-device cnn personalisation
Ilias Leontiadis, Stefanos Laskaridis, Stylianos I Venieris, and Nicholas D Lane · 2021
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Fine-grained elastic partitioning for distributed dnn towards mobile web ar services in the 5g era
Pei Ren, Xiuquan Qiao, Yakun Huang, Ling Liu, Calton Pu, and Schahram Dustdar · 2021
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To bridge neural network design and real-world performance: A behaviour study for neural networks
Xiaohu Tang, Shihao Han, Li Lyna Zhang, Ting Cao, and Yunxin Liu · 2021
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Compute shader in image processing development
Robert Tornai and Péter Fürjes-Benke · 2021
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Asymo: scalable and efficient deep-learning inference on asymmetric mobile cpus
Manni Wang, Shaohua Ding, Ting Cao, Yunxin Liu, and Fengyuan Xu · 2021
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Exploring deep reuse in winograd cnn inference
Ruofan Wu, Feng Zhang, Zhen Zheng, Xiaoyong Du, and Xipeng Shen · 2021
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From cloud to edge: a first look at public edge platforms
Mengwei Xu, Zhe Fu, Xiao Ma, Li Zhang, Yanan Li, Feng Qian, Shangguang Wang, Ke Li, Jingyu Yang, and Xuanzhe Liu · 2021
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Video analytics with zero-streaming cameras
Mengwei Xu, Tiantu Xu, Yunxin Liu, and Felix Xiaozhu Lin · 2021
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