Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

10.1109/LWC.2019.2894703

hadidi, 2018, Musical chair: Efficient real-time recognition using collaborative IoT devices, arXiv 1802 02138

islam, 2019, Zygarde: Time-sensitive on-device deep intelligence on intermittently-powered systems, arXiv 1905 03854

10.1109/RTSS.2018.00017

10.1038/nature20101

10.1145/3007787.3001165

xu, 2017, Enabling cooperative inference of deep learning on wearables and smartphones, arXiv 1712 03073

10.1109/MNET.2018.1700202

eshratifar, 2018, JointDNN: An efficient training and inference engine for intelligent mobile cloud computing services, arXiv 1801 08618

10.1145/3093337.3037698

10.1109/MNET.2018.1700145

10.1109/ICPR.2016.7900006

10.1109/JIOT.2018.2846644

10.1109/JSAC.2018.2869954

10.1109/TVT.2018.2846232

10.1109/MCOM.2018.1701080

10.1109/JPROC.2019.2918951

2017, Augmented and Virtual Reality The First Wave of 5G Killer Apps

10.1145/3089801.3089804

10.1109/CVPR.2016.521

iandola, 2016, SqueezeNet: AlexNet-level accuracy with 50 $\times$ fewer parameters and <0.5 MB model size, arXiv 1602 07360

kim, 2019, $\mu$ Layer: Low latency on-device inference using cooperative single-layer acceleration and processor-friendly quantization, 14th European Conf Proc, 45

10.3115/v1/P15-2116

pham, 2018, Efficient neural architecture search via parameter sharing, arXiv 1802 03268

van den oord, 2016, WaveNet: A generative model for raw audio, arXiv 1609 03499

10.1109/COMST.2017.2745201

10.1145/3020078.3021744

10.4018/IJCAC.2018040105

10.1109/COMST.2017.2682318

10.1109/JIOT.2016.2579198

10.1109/CVPR.2015.7298594

10.1093/comjnl/bxx019

10.1145/3229556.3229562

10.1109/LCOMM.2016.2601087

10.1145/2750858.2804262

mulhollon, 2004, Wondershaper

kulick, 2013, Bayesian Changepoint Detection

han, 2015, Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding, arXiv 1510 00149 [cs]

krizhevsky, 2009, Learning multiple layers of features from tiny images

howard, 2017, MobileNets: Efficient convolutional neural networks for mobile vision applications, arXiv 1704 04861

krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Proc Adv Neural Inf Process Syst, 1097

10.4108/eai.30-11-2016.2267463

tokui, 2015, Chainer: A next-generation open source framework for deep learning, Proc 29th Annu Conf Neural Inf Process Syst, 1

kim, 2015, Compression of deep convolutional neural networks for fast and low power mobile applications, arXiv 1511 06530

adams, 2007, Bayesian online changepoint detection, arXiv 0710 3742

liu, 2018, Progressive neural architecture search, Proc Eur Conf Comput Vis (ECCV), 19

10.1145/3230543.3230558

cai, 2018, Efficient architecture search by network transformation, Proc 32nd AAAI Conf Artif Intell, 2787