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Deep neural networks with large model sizes achieve state-of-the-art results for tasks in computer vision (CV) and natural language processing (NLP).
A survey on smart home networking
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Yolo9000: better, faster, stronger
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The emergence of edge computing
Mahadev Satyanarayanan · 2017
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Edge computing for the internet of things: A case study
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Image semantic segmentation based on convolutional neural network and conditional random field
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A systematic dnn weight pruning framework using alternating direction method of multipliers
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Deepthings: Distributed adaptive deep learning inference on resource-constrained iot edge clusters
Zhuoran Zhao, Kamyar Mirzazad Barijough, and Andreas Gerstlauer · 2018
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The dcomp testbed
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Gpipe: Efficient training of giant neural networks using pipeline parallelism
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V Le, Yonghui Wu, et al · 2019
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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, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Megatron-lm: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2019
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Pixel-adaptive convolutional neural networks
Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, and Jan Kautz · 2019
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Lite transformer with long-short range attention
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Dnnoff: Offloading dnn-based intelligent iot applications in mobile edge computing
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