[情報] Mobile Deep learning Resource

看板DataScience作者aa155495 (冷月狂刃)時間6年前 (2018/03/04 18:46)推噓8(8推 0噓 2→)

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最近在做適合跑在嵌入式或手機上的模型來整理一下相關研究資源好了 =================================================== Survey paper A Survey of Model Compression and Acceleration for Deep Neural Networks [arXiv '17] https://arxiv.org/abs/1710.09282 -------------------------------------------------------- 輕量化 Model 1. MobilenetV2: Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation [arXiv '18, Google] https://arxiv.org/pdf/1801.04381.pdf 2. NasNet: Learning Transferable Architectures for Scalable Image Recognition [arXiv '17, Google] 註：Google AutoML 的論文 https://arxiv.org/pdf/1707.07012.pdf 3. DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices [AAAI'18, Samsung] https://arxiv.org/abs/1708.04728 4. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices [arXiv '17, Megvii] https://arxiv.org/abs/1707.01083 5. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications [arXiv '17, Google] https://arxiv.org/abs/1704.04861 6. CondenseNet: An Efficient DenseNet using Learned Group Convolutions [arXiv '17] https://arxiv.org/abs/1711.09224 ------------------------------------------------------------ System 1. DeepMon: Mobile GPU-based Deep Learning Framework for Continuous Vision Applications [MobiSys '17] https://www.sigmobile.org/mobisys/2017/accepted.php 2. DeepEye: Resource Efficient Local Execution of Multiple Deep Vision Models using Wearable Commodity Hardware [MobiSys '17] http://fahim-kawsar.net/papers/Mathur.MobiSys2017-Camera.pdf 3. MobiRNN: Efficient Recurrent Neural Network Execution on Mobile GPU [EMDL '17] https://arxiv.org/abs/1706.00878 4. DeepSense: A GPU-based deep convolutional neural network framework on commodity mobile devices [WearSys '16] http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=4278&context=sis_research 5. DeepX: A Software Accelerator for Low-Power Deep Learning Inference on Mobile Devices [IPSN '16] http://niclane.org/pubs/deepx_ipsn.pdf 6. EIE: Efficient Inference Engine on Compressed Deep Neural Network [ISCA '16] https://arxiv.org/abs/1602.01528 7. MCDNN: An Approximation-Based Execution Framework for Deep Stream Processing Under Resource Constraints [MobiSys '16] http://haneul.github.io/papers/mcdnn.pdf 8. DXTK: Enabling Resource-efficient Deep Learning on Mobile and Embedded Devices with the DeepX Toolkit [MobiCASE '16] 9. Sparsification and Separation of Deep Learning Layers for Constrained Resource Inference on Wearables [SenSys ’16] 10. An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices [IoT-App ’15] 11. CNNdroid: GPU-Accelerated Execution of Trained Deep Convolutional Neural Networks on Android [MM '16] 12. fpgaConvNet: A Toolflow for Mapping Diverse Convolutional Neural Networks on Embedded FPGAs [NIPS '17] -------------------------------------------------------------- Quantization (Model compression) 1. The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning [ICML'17] 2. Compressing Deep Convolutional Networks using Vector Quantization [arXiv'14] 3. Quantized Convolutional Neural Networks for Mobile Devices [CVPR '16] 4. Fixed-Point Performance Analysis of Recurrent Neural Networks [ICASSP'16] 5. Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations [arXiv'16] 6. Loss-aware Binarization of Deep Networks [ICLR'17] 7. Towards the Limit of Network Quantization [ICLR'17] 8. Deep Learning with Low Precision by Half-wave Gaussian Quantization [CVPR'17] 9. ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks [arXiv'17] 10. Training and Inference with Integers in Deep Neural Networks [ICLR'18] ------------------------------------------------------------ Pruning (Model Compression) 1. Learning both Weights and Connections for Efficient Neural Networks [NIPS'15] 2. Pruning Filters for Efficient ConvNets [ICLR'17] 3. Pruning Convolutional Neural Networks for Resource Efficient Inference [ICLR'17] 4. Soft Weight-Sharing for Neural Network Compression [ICLR'17] 5. Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding [ICLR'16] 6. Dynamic Network Surgery for Efficient DNNs [NIPS'16] 7. Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning [CVPR'17] 8. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression [ICCV'17] 9. To prune, or not to prune: exploring the efficacy of pruning for model compression [ICLR'18] --------------------------------------------------------------- Approximation 1. Efficient and Accurate Approximations of Nonlinear Convolutional Networks [CVPR'15] 2. Accelerating Very Deep Convolutional Networks for Classification and Detection (Extended version of above one) 3. Convolutional neural networks with low-rank regularization [arXiv'15] 4. Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation [NIPS'14] 5. Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications [ICLR'16] 6. High performance ultra-low-precision convolutions on mobile devices [NIPS'17] 先發PAPER的整理好了之後有空再整理其他部分 -- ※ 發信站: 批踢踢實業坊(ptt.cc), 來自: 114.25.14.8 ※ 文章網址: https://www.ptt.cc/bbs/deeplearning/M.1520160416.A.80C.html ※ 編輯: aa155495 (114.25.14.8), 03/04/2018 18:50:42