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Pan, \"Learning to prune deep neural networks via layer-wise optimal brain surgeon,\" in NeurIPS, 2017."},{"key":"e_1_3_2_2_6_1","volume-title":"Zhou et al., \"Rethinking the value of network pruning,\" in ICLR","author":"Liu Z.","year":"2018","unstructured":"Z. Liu , M. Sun , T. Zhou et al., \"Rethinking the value of network pruning,\" in ICLR , 2018 . Z. Liu, M. Sun, T. Zhou et al., \"Rethinking the value of network pruning,\" in ICLR, 2018."},{"key":"e_1_3_2_2_7_1","volume-title":"Zhang et al., \"A systematic weight pruning of dnns using alternating direction method of multipliers,\" in ECCV","author":"Zhang T.","year":"2018","unstructured":"T. Zhang , S. Ye , Y. Zhang et al., \"A systematic weight pruning of dnns using alternating direction method of multipliers,\" in ECCV , 2018 . T. Zhang, S. Ye, Y. Zhang et al., \"A systematic weight pruning of dnns using alternating direction method of multipliers,\" in ECCV, 2018."},{"key":"e_1_3_2_2_8_1","volume-title":"Ye et al., \"Admm-nn: an algorithm-hardware co-design framework of dnns using alternating direction methods of multipliers,\" in ASPLOS","author":"Ren A.","year":"2019","unstructured":"A. Ren , T. Zhang , S. Ye et al., \"Admm-nn: an algorithm-hardware co-design framework of dnns using alternating direction methods of multipliers,\" in ASPLOS , 2019 . A. Ren, T. Zhang, S. Ye et al., \"Admm-nn: an algorithm-hardware co-design framework of dnns using alternating direction methods of multipliers,\" in ASPLOS, 2019."},{"key":"e_1_3_2_2_9_1","volume-title":"Wang et al., \"Learning structured sparsity in deep neural networks,\" in NeurIPS","author":"Wen W.","year":"2016","unstructured":"W. Wen , C. Wu , Y. Wang et al., \"Learning structured sparsity in deep neural networks,\" in NeurIPS , 2016 . W. Wen, C. Wu, Y. 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Zhang et al., \"Variational convolutional neural network pruning,\" in CVPR, 2019."},{"key":"e_1_3_2_2_14_1","volume-title":"Improving deep neural network sparsity through decorrelation regularization,\" in IJCAI","author":"Zhu X.","year":"2018","unstructured":"X. Zhu , W. Zhou , and H. Li , \" Improving deep neural network sparsity through decorrelation regularization,\" in IJCAI , 2018 . X. Zhu, W. Zhou, and H. Li, \"Improving deep neural network sparsity through decorrelation regularization,\" in IJCAI, 2018."},{"key":"e_1_3_2_2_15_1","volume-title":"Shen et al., \"Learning efficient convolutional networks through network slimming,\" in ICCV","author":"Liu Z.","year":"2017","unstructured":"Z. Liu , J. Li , Z. Shen et al., \"Learning efficient convolutional networks through network slimming,\" in ICCV , 2017 . Z. Liu, J. Li, Z. 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Babu, \"Data-free parameter pruning for deep neural networks,\" in BMVC, 2015."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/PROC.1967.6011"},{"key":"e_1_3_2_2_23_1","volume-title":"Advanced Compiler Design and Implementation .hskip 1em plus 0.5em minus 0.4emrelax Morgan Kaufmann Publishers Inc","author":"Muchnick S.","year":"1997","unstructured":"S. Muchnick , Advanced Compiler Design and Implementation .hskip 1em plus 0.5em minus 0.4emrelax Morgan Kaufmann Publishers Inc ., 1997 . S. Muchnick, Advanced Compiler Design and Implementation .hskip 1em plus 0.5em minus 0.4emrelax Morgan Kaufmann Publishers Inc., 1997."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"crossref","unstructured":"Coleman Stephanie and McKinley Kathryn S. \"Tile Size Selection Using Cache Organization and Data Layout \" in PLDI 1995.  Coleman Stephanie and McKinley Kathryn S. \"Tile Size Selection Using Cache Organization and Data Layout \" in PLDI 1995.","DOI":"10.1145\/207110.207162"},{"key":"e_1_3_2_2_25_1","unstructured":"https:\/\/www.tensorflow.org\/lite\/performance\/model_optimization.  https:\/\/www.tensorflow.org\/lite\/performance\/model_optimization."},{"key":"e_1_3_2_2_26_1","volume-title":"Jiang et al., \"TVM: An automated end-to-end optimizing compiler for deep learning,\" in OSDI","author":"Chen T.","year":"2018","unstructured":"T. Chen , T. Moreau , Z. Jiang et al., \"TVM: An automated end-to-end optimizing compiler for deep learning,\" in OSDI , 2018 . T. Chen, T. Moreau, Z. 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