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Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago.
Backpropagation applied to handwritten zip code recognition
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Activity recognition using cell phone accelerometers
Kwapisz, J.R., Weiss, G.M., Moore, S.A.: · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Guihot, H.: · 2012
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Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., Dean, J.: · 2013
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Recursive deep models for semantic compositionality over a sentiment treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C.D., Ng, A., Potts, C.: · 2013
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Sutskever, I., Vinyals, O., Le, Q.V.: · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., Bengio, Y.: · 2014
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Hexagon dsp: An architecture optimized for mobile multimedia and communications
Codrescu, L., Anderson, W., Venkumanhanti, S., Zeng, M., Plondke, E., Koob, C., Ingle, A., Tabony, C., Maule, R.: · 2014
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Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
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cudnn: Efficient primitives for deep learning
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Wu, Y., Lim, J., Yang, M.H.: · 2015
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A convolutional neural network cascade for face detection
Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G.: · 2015
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Facenet: A unified embedding for face recognition and clustering
Schroff, F., Kalenichenko, D., Philbin, J.: · 2015
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Appearance-based gaze estimation in the wild
Zhang, X., Sugano, Y., Fritz, M., Bulling, A.: · 2015
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Twitter sentiment analysis with deep convolutional neural networks
Severyn, A., Moschitti, A.: · 2015
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Can deep learning revolutionize mobile sensing?
Lane, N.D., Georgiev, P.: · 2015
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: · 2016
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Image super-resolution using deep convolutional networks
Dong, C., Loy, C.C., He, K., Tang, X.: · 2016
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The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
Cited alongside, same era.
Deep convolutional and lstm recurrent neural networks for multimodal wearable activity recognition
Ordóñez, F.J., Roggen, D.: · 2016
Cited alongside, same era.
Sleep quality prediction from wearable data using deep learning
Sathyanarayana, A., Joty, S., Fernandez-Luque, L., Ofli, F., Srivastava, J., Elmagarmid, A., Arora, T., Taheri, S.: · 2016
https://www.tensorflow.org/mobile/mobile_intro
TensorFlow-Mobile: · 2018
Closest in time.
Cappuccino: Efficient cnn inference software synthesis for mobile system-on-chips
Motamedi, M., Fong, D., Ghiasi, S.: · 2018
Closest in time.
https://developer.qualcomm.com/docs/snpe/overview.html
SNPE: · 2018
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https://developer.huawei.com/consumer/en/devservice/doc/2020315
HiAI: · 2018
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Techology trend of edge ai
Lee, Y.L., Tsung, P.K., Wu, M.: · 2018
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https://developer.android.com/ndk/guides/neuralnetworks/
NNAPI: · 2018
Closest in time.
https://github.com/arm-software/armnn
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Cnndroid: Gpu-accelerated execution of trained deep convolutional neural networks on android
Latifi Oskouei, S.S., Golestani, H., Hashemi, M., Ghiasi, S.: · 2016
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al.: · 2016
Cited alongside, same era.
Accurate image super-resolution using very deep convolutional networks
Kim, J., Kwon Lee, J., Mu Lee, K.: · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., Fei-Fei, L.: · 2016
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
Cited alongside, same era.
Dslr-quality photos on mobile devices with deep convolutional networks
Ignatov, A., Kobyshev, N., Timofte, R., Vanhoey, K., Van Gool, L.: · 2017
Cited alongside, same era.
ArmNN: · 2018
Closest in time.
https://github.com/soumith/torch-android
Torch-Android: · 2018
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https://deeplearning4j.org/docs/latest/deeplearning4j-android
Deeplearning4j: · 2018
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https://www.tensorflow.org/mobile/tflite/
TensorFlow-Lite: · 2018
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https://github.com/sh1r0/caffe-android-lib
Caffe-Android: · 2018
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https://caffe2.ai/docs/mobile-integration.html
Caffe2-Android: · 2018
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https://github.com/leliana/whatsthis
MXNet: · 2018
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https://github.com/sony/nnabla
NNabla: · 2018
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https://www.tensorflow.org/mobile/
TensorFlow-Mobile/Lite: · 2018
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https://github.com/caffe2/aicamera
Caffe2-AICamera-Demo: · 2018
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https://www.tensorflow.org/mobile/tflite/performance
TFLite-Benchmark: · 2018
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https://www.slideshare.net/kstan2/caffe2-on-android
Caffe2-Presentation: · 2018
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A quantization-friendly separable convolution for mobilenets
Sheng, T., Feng, C., Zhuo, S., Zhang, X., Shen, L., Aleksic, M.: · 2018
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https://github.com/davidsandberg/facenet
FaceNet-github: · 2018
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https://github.com/tensorflow/models/tree/master/research/slim
TF-Slim: · 2018
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Towards total scene understanding: Classification, annotation and segmentation in an automatic framework
Li, L.J., Socher, R., Fei-Fei, L.: · 2043
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Convolutional neural network architectures for matching natural language sentences
Hu, B., Lu, Z., Li, H., Chen, Q.: · 2050
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