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Accessed: 01-14-2018. C3D: Generic Features for Video Analysis. http:\/\/vlg.cs.dartmouth.edu\/c3d\/. (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_5_1","unstructured":"Accessed: 01-14-2018. ILSVRC-2014 model (VGG team) with 16 weight layers. https:\/\/gist.github.com\/ksimonyan\/211839e770f7b538e2d8. (Accessed: 01-14-2018).  Accessed: 01-14-2018. ILSVRC-2014 model (VGG team) with 16 weight layers. https:\/\/gist.github.com\/ksimonyan\/211839e770f7b538e2d8. (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_6_1","unstructured":"Accessed: 01-14-2018. Imagenet Winners Benchmarking. https:\/\/github.com\/soumith\/convnet-benchmarks. (Accessed: 01-14-2018).  Accessed: 01-14-2018. Imagenet Winners Benchmarking. https:\/\/github.com\/soumith\/convnet-benchmarks. (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_7_1","unstructured":"Accessed: 01-14-2018. Intel\u00ae Intrinsics Guide. https:\/\/software.intel.com\/sites\/landingpage\/IntrinsicsGuide\/. (Accessed: 01-14-2018).  Accessed: 01-14-2018. Intel\u00ae Intrinsics Guide. https:\/\/software.intel.com\/sites\/landingpage\/IntrinsicsGuide\/. (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_8_1","volume-title":"01-14-2018","author":"Accessed","year":"2018","unstructured":"Accessed : 01-14-2018 . Intel\u00ae Math Kernel Library . https:\/\/software.intel.com\/en-us\/mkl. (Accessed: 01-14- 2018 ). Accessed: 01-14-2018. Intel\u00ae Math Kernel Library. https:\/\/software.intel.com\/en-us\/mkl. (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_9_1","unstructured":"Accessed: 01-14-2018. Intel\u00ae Nervana reference deep learning framework. https:\/\/github.com\/NervanaSystems\/neon. (Accessed: 01-14-2018).  Accessed: 01-14-2018. Intel\u00ae Nervana reference deep learning framework. https:\/\/github.com\/NervanaSystems\/neon. (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_10_1","unstructured":"Accessed: 01-14-2018. LIBXSMM. https:\/\/github.com\/hfp\/libxsmm. (Accessed: 01-14-2018).  Accessed: 01-14-2018. LIBXSMM. https:\/\/github.com\/hfp\/libxsmm. (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_11_1","unstructured":"Accessed: 01-14-2018. MCDRAM as High-Bandwidth Memory (HBM) in Knights Landing Processors: Developer's Guide. \"https: \/\/colfaxresearch.com\/knl-mcdram\/\". (Accessed: 01-14-2018).  Accessed: 01-14-2018. MCDRAM as High-Bandwidth Memory (HBM) in Knights Landing Processors: Developer's Guide. \"https: \/\/colfaxresearch.com\/knl-mcdram\/\". (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_12_1","unstructured":"Accessed: 01-14-2018. SPIRAL Project: Fast x86 Barrier. http:\/\/www.spiral.net\/software\/barrier.html. (Accessed: 01-14-2018).  Accessed: 01-14-2018. SPIRAL Project: Fast x86 Barrier. http:\/\/www.spiral.net\/software\/barrier.html. (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_13_1","unstructured":"Accessed: 01-14-2018. Wincnn. \"https:\/\/github.com\/andravin\/wincnn\". (Accessed: 01-14-2018).  Accessed: 01-14-2018. Wincnn. \"https:\/\/github.com\/andravin\/wincnn\". (Accessed: 01-14-2018)."},{"key":"e_1_3_2_2_14_1","volume-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","author":"Badrinarayanan Vijay","year":"2017","unstructured":"Vijay Badrinarayanan , Alex Kendall , and Roberto Cipolla . 2017. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation . IEEE Transactions on Pattern Analysis and Machine Intelligence ( 2017 ). Vijay Badrinarayanan, Alex Kendall, and Roberto Cipolla. 2017. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)."},{"key":"e_1_3_2_2_15_1","volume-title":"Shraman Ray Chaudhuri, and Nir Shavit","author":"Budden David","year":"2016","unstructured":"David Budden , Alexander Matveev , Shibani Santurkar , Shraman Ray Chaudhuri, and Nir Shavit . 2016 . Deep Tensor Convolution on Multi-cores . arXiv preprint arXiv:1611.06565 (2016). 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Fast training of convolutional networks through ffts. arXiv preprint arXiv:1312.5851 (2013)."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2015.7139679"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2015.7353481"},{"key":"e_1_3_2_2_40_1","unstructured":"Guido F Montufar Razvan Pascanu Kyunghyun Cho and Yoshua Bengio. 2014. On the number of linear regions of deep neural networks. In Advances in neural information processing systems. 2924--2932.   Guido F Montufar Razvan Pascanu Kyunghyun Cho and Yoshua Bengio. 2014. On the number of linear regions of deep neural networks. In Advances in neural information processing systems. 2924--2932."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.178"},{"key":"e_1_3_2_2_42_1","volume-title":"David GC Hilderbrand, and Won-Ki Jeong","author":"Quan Tran Minh","year":"2016","unstructured":"Tran Minh Quan , David GC Hilderbrand, and Won-Ki Jeong . 2016 . 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Kevin Vincent Kevin Stephano Michael Frumkin Boris Ginsburg and Julien Demouth. 2017. On Improving the Numerical Stability of Winograd Convolutions. (2017)."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/2807591.2807631"},{"key":"e_1_3_2_2_55_1","volume-title":"Arithmetic complexity of computations","author":"Winograd Shmuel","unstructured":"Shmuel Winograd . 1980. Arithmetic complexity of computations . Vol. 33 . Siam . Shmuel Winograd. 1980. Arithmetic complexity of computations. Vol. 33. Siam."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS.2016.119"},{"key":"e_1_3_2_2_57_1","volume-title":"SC16: International Conference for. IEEE, 854--865","author":"Zlateski Aleksandar","year":"2016","unstructured":"Aleksandar Zlateski , Kisuk Lee , and H Sebastian Seung . 2016 . ZNNi: maximizing the inference throughput of 3D convolutional networks on CPUs and GPUs. 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