SeerNet: Predicting Convolutional Neural Network Feature-Map Sparsity Through Low-Bit Quantization
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Wencong Xiao | Yunxin Liu | Chen Zhang | Lanshun Nie | Shijie Cao | Lintao Zhang | Lingxiao Ma | Zhi Yang | Lintao Zhang | Wencong Xiao | Yunxin Liu | Zhi Yang | Lingxiao Ma | Lanshun Nie | Shijie Cao | Chen Zhang
[1] Chen Zhang,et al. Balanced Sparsity for Efficient DNN Inference on GPU , 2018, AAAI.
[2] Bo Li,et al. SECOND: Sparsely Embedded Convolutional Detection , 2018, Sensors.
[3] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[4] Ian D. Reid,et al. Towards Effective Low-Bitwidth Convolutional Neural Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[5] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[6] Song Han,et al. ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA , 2016, FPGA.
[7] Tao Li,et al. Prediction Based Execution on Deep Neural Networks , 2018, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).
[8] Xiaoxiao Li,et al. Not All Pixels Are Equal: Difficulty-Aware Semantic Segmentation via Deep Layer Cascade , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[9] Vincent Vanhoucke,et al. Improving the speed of neural networks on CPUs , 2011 .
[10] Yi Yang,et al. More is Less: A More Complicated Network with Less Inference Complexity , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Sergey Ioffe,et al. Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[12] G. Hua,et al. LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks , 2018, ECCV.
[13] Ali Farhadi,et al. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.
[14] Jianxiong Xiao,et al. 3D ShapeNets: A deep representation for volumetric shapes , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Shuchang Zhou,et al. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients , 2016, ArXiv.
[16] Fuchun Sun,et al. RON: Reverse Connection with Objectness Prior Networks for Object Detection , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Xiaogang Wang,et al. PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[18] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[19] William J. Dally,et al. SCNN: An accelerator for compressed-sparse convolutional neural networks , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).
[20] Hassan Foroosh,et al. Sparse Convolutional Neural Networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Li Zhang,et al. Spatially Adaptive Computation Time for Residual Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[22] Raghuraman Krishnamoorthi,et al. Quantizing deep convolutional networks for efficient inference: A whitepaper , 2018, ArXiv.
[23] Charbel Sakr,et al. PredictiveNet: An energy-efficient convolutional neural network via zero prediction , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).
[24] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[25] Yiran Chen,et al. Learning Structured Sparsity in Deep Neural Networks , 2016, NIPS.
[26] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[27] Bin Yang,et al. SBNet: Sparse Blocks Network for Fast Inference , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[28] Roberto Cipolla,et al. Deep Roots: Improving CNN Efficiency with Hierarchical Filter Groups , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[29] Andrew Zisserman,et al. Speeding up Convolutional Neural Networks with Low Rank Expansions , 2014, BMVC.
[30] Laurens van der Maaten,et al. Submanifold Sparse Convolutional Networks , 2017, ArXiv.
[31] Shaohuai Shi,et al. Speeding up Convolutional Neural Networks By Exploiting the Sparsity of Rectifier Units , 2017, ArXiv.
[32] Rajesh K. Gupta,et al. SnaPEA: Predictive Early Activation for Reducing Computation in Deep Convolutional Neural Networks , 2018, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).
[33] Song Han,et al. Trained Ternary Quantization , 2016, ICLR.
[34] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[35] Hanan Samet,et al. Pruning Filters for Efficient ConvNets , 2016, ICLR.
[36] Patrick Judd,et al. Cnvlutin2: Ineffectual-Activation-and-Weight-Free Deep Neural Network Computing , 2017, ArXiv.
[37] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.