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This paper proposes Mandheling, the first system that enables highly resource-efficient on-device training by orchestrating the mixed-precision training with on-chip Digital Signal Processing (DSP) offloading.
Dynamic memory optimization using pool allocation and prefetching
Qin Zhao, Rodric Rabbah, and Weng-Fai Wong · 2005
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Performance driven data cache prefetching in a dynamic software optimization system
Jean Christophe Beyler and Philippe Clauss · 2007
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Operating system integrated energy aware scratchpad allocation strategies for multiprocess applications
Robert Pyka, Christoph Faßbach, Manish Verma, Heiko Falk, and Peter Marwedel · 2007
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Neon technology introduction
Venu Gopal Reddy · 2008
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Large-scale machine learning with stochastic gradient descent
Léon Bottou · 2010
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Mmt: Exploiting fine-grained parallelism in dynamic memory management
Devesh Tiwari, Sanghoon Lee, James Tuck, and Yan Solihin · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Dsp. ear: Leveraging co-processor support for continuous audio sensing on smartphones
Petko Georgiev, Nicholas D Lane, Kiran K Rachuri, and Cecilia Mascolo · 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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Binaryconnect: Training deep neural networks with binary weights during propagations
Matthieu Courbariaux, Yoshua Bengio, and Jean-Pierre David · 2015
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Deepear: robust smartphone audio sensing in unconstrained acoustic environments using deep learning
Nicholas D Lane, Petko Georgiev, and Lorena Qendro · 2015
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Neural networks with few multiplications
Zhouhan Lin, Matthieu Courbariaux, Roland Memisevic, and Yoshua Bengio · 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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Mcdnn: An approximation-based execution framework for deep stream processing under resource constraints
Seungyeop Han, Haichen Shen, Matthai Philipose, Sharad Agarwal, Alec Wolman, and Arvind Krishnamurthy · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Federated learning: Strategies for improving communication efficiency
Jakub Konečnỳ, H Brendan McMahan, Felix X Yu, Peter Richtárik, Ananda Theertha Suresh, and Dave Bacon · 2016
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Deepx: A software accelerator for low-power deep learning inference on mobile devices
Nicholas D Lane, Sourav Bhattacharya, Petko Georgiev, Claudio Forlivesi, Lei Jiao, Lorena Qendro, and Fahim Kawsar · 2016
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A survey of recent prefetching techniques for processor caches
Sparsh Mittal · 2016
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Opportunistic and context-aware affect sensing on smartphones
Rajib Rana, Margee Hume, John Reilly, Raja Jurdak, and Jeffrey Soar · 2016
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Xnor-net: Imagenet classification using binary convolutional neural networks
Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi · 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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Binarized neural networks on the imagenet classification task
Xundong Wu, Yong Wu, and Yong Zhao · 2016
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Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients
Shuchang Zhou, Yuxin Wu, Zekun Ni, Xinyu Zhou, He Wen, and Yuheng Zou · 2016
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Chenzhuo Zhu, Song Han, Huizi Mao, and William J Dally · 2016
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https://ai.googleblog.com/2017/04/federated-learning-collaborative.html , 2017
Federated learning: Collaborative machine learning without centralized training data · 2017
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Fxpnet: Training a deep convolutional neural network in fixed-point representation
Xi Chen, Xiaolin Hu, Hucheng Zhou, and Ningyi Xu · 2017
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Accelerating mobile audio sensing algorithms through on-chip gpu offloading
Petko Georgiev, Nicholas D Lane, Cecilia Mascolo, and David Chu · 2017
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Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Deepmon: Mobile gpu-based deep learning framework for continuous vision applications
Loc N Huynh, Youngki Lee, and Rajesh Krishna Balan · 2017
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Towards accurate binary convolutional neural network
Xiaofan Lin, Cong Zhao, and Wei Pan · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Don’t decay the learning rate, increase the batch size
Samuel L Smith, Pieter-Jan Kindermans, Chris Ying, and Quoc V Le · 2017
Cited alongside, same era.
Fedpaq: A communication-efficient federated learning method with periodic averaging and quantization
Amirhossein Reisizadeh, Aryan Mokhtari, Hamed Hassani, Ali Jadbabaie, and Ramtin Pedarsani · 2020
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Federated learning with quantization constraints
Nir Shlezinger, Mingzhe Chen, Yonina C Eldar, H Vincent Poor, and Shuguang Cui · 2020
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Niti: Training integer neural networks using integer-only arithmetic
Maolin Wang, Seyedramin Rasoulinezhad, Philip HW Leong, and Hayden KH So · 2020
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Training high-performance and large-scale deep neural networks with full 8-bit integers
Yukuan Yang, Lei Deng, Shuang Wu, Tianyi Yan, Yuan Xie, and Guoqi Li · 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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Scalable methods for 8-bit training of neural networks
Ron Banner, Itay Hubara, Elad Hoffer, and Daniel Soudry · 2018
Cited alongside, same era.
T V M {TVM} : An automated end-to-end optimizing compiler for deep learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang, Lianmin Zheng, Eddie Yan, Haichen Shen, Meghan Cowan, Leyuan Wang, Yuwei Hu, Luis Ceze, et al · 2018
Cited alongside, same era.
Nestdnn: Resource-aware multi-tenant on-device deep learning for continuous mobile vision
Biyi Fang, Xiao Zeng, and Mi Zhang · 2018
Cited alongside, same era.
Federated learning for mobile keyboard prediction
Andrew Hard, Kanishka Rao, Rajiv Mathews, Swaroop Ramaswamy, Françoise Beaufays, Sean Augenstein, Hubert Eichner, Chloé Kiddon, and Daniel Ramage · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Relaxed quantization for discretized neural networks
Christos Louizos, Matthias Reisser, Tijmen Blankevoort, Efstratios Gavves, and Max Welling · 2018
Cited alongside, same era.
Communication compression for decentralized training
Hanlin Tang, Shaoduo Gan, Ce Zhang, Tong Zhang, and Ji Liu · 2018
Cited alongside, same era.
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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Fixed-point back-propagation training
Xishan Zhang, Shaoli Liu, Rui Zhang, Chang Liu, Di Huang, Shiyi Zhou, Jiaming Guo, Qi Guo, Zidong Du, Tian Zhi, et al · 2020
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Towards lower bit multiplication for convolutional neural network training
Kai Zhong, Tianchen Zhao, Xuefei Ning, Shulin Zeng, Kaiyuan Guo, Yu Wang, and Huazhong Yang · 2020
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Towards unified int8 training for convolutional neural network
Feng Zhu, Ruihao Gong, Fengwei Yu, Xianglong Liu, Yanfei Wang, Zhelong Li, Xiuqi Yang, and Junjie Yan · 2020
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https://developer.qualcomm.com/software/hexagon-dsp-sdk/dsp-processor , 2021
dsp-processor · 2021
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https://gdpr-info.eu/ , 2021
General data protection regulation · 2021
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https://genshin.mihoyo.com/ , 2021
Genshin · 2021
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https://developer.qualcomm.com/software/hexagon-dsp-sdk , 2021
/hexagon-dsp-sdk · 2021
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https://en.wikipedia.org/wiki/Qualcomm_Hexagon , 2021
Qualcomm hexagon · 2021
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https://www.tiktok.com , 2021
Tiktok · 2021
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https://www.youtube.com , 2021
Youtube · 2021
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Accelerating on-device learning with layer-wise processor selection method on unified memory
Donghee Ha, Mooseop Kim, KyeongDeok Moon, and Chi Yoon Jeong · 2021
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Legodnn: block-grained scaling of deep neural networks for mobile vision
Rui Han, Qinglong Zhang, Chi Harold Liu, Guoren Wang, Jian Tang, and Lydia Y Chen · 2021
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Adaptive quantization of model updates for communication-efficient federated learning
Divyansh Jhunjhunwala, Advait Gadhikar, Gauri Joshi, and Yonina C Eldar · 2021
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Advances and open problems in federated learning
Peter Kairouz, H Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, et al · 2021
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Hermes: an efficient federated learning framework for heterogeneous mobile clients
Ang Li, Jingwei Sun, Pengcheng Li, Yu Pu, Hai Li, and Yiran Chen · 2021
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Ppfl: privacy-preserving federated learning with trusted execution environments
Fan Mo, Hamed Haddadi, Kleomenis Katevas, Eduard Marin, Diego Perino, and Nicolas Kourtellis · 2021
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A comprehensive survey on training acceleration for large machine learning models in iots
Haozhao Wang, Zhihao Qu, Qihua Zhou, Haobo Zhang, Boyuan Luo, Wenchao Xu, Song Guo, and Ruixuan Li · 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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Characterizing impacts of heterogeneity in federated learning upon large-scale smartphone data
Chengxu Yang, Qipeng Wang, Mengwei Xu, Zhenpeng Chen, Kaigui Bian, Yunxin Liu, and Xuanzhe Liu · 2021
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Energy-efficient resource management for federated edge learning with cpu-gpu heterogeneous computing
Qunsong Zeng, Yuqing Du, Kaibin Huang, and Kin K Leung · 2021
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Octo: { \{ INT8 } \} training with loss-aware compensation and backward quantization for tiny on-device learning
Qihua Zhou, Song Guo, Zhihao Qu, Jingcai Guo, Zhenda Xu, Jiewei Zhang, Tao Guo, Boyuan Luo, and Jingren Zhou · 2021
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A comprehensive benchmark of deep learning libraries on mobile devices
Qiyang Zhang, Xiang Li, Xiangying Che, Xiao Ma, Ao Zhou, Mengwei Xu, Shangguang Wang, Yun Ma, and Xuanzhe Liu · 2022
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