Post training 4-bit quantization of convolutional networks for rapid-deployment
暂无分享,去创建一个
[1] D.M. Mount,et al. An Efficient k-Means Clustering Algorithm: Analysis and Implementation , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[2] David L. Neuhoff,et al. The validity of the additive noise model for uniform scalar quantizers , 2005, IEEE Transactions on Information Theory.
[3] Raul H. C. Lopes,et al. Pengaruh Latihan Small Sided Games 4 Lawan 4 Dengan Maksimal Tiga Sentuhan Terhadap Peningkatan VO2MAX Pada Siswa SSB Tunas Muda Bragang Klampis U-15 , 2022, Jurnal Ilmiah Mandala Education.
[4] Ron Meir,et al. Expectation Backpropagation: Parameter-Free Training of Multilayer Neural Networks with Continuous or Discrete Weights , 2014, NIPS.
[5] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[6] Shuchang Zhou,et al. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients , 2016, ArXiv.
[7] Ali Farhadi,et al. XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks , 2016, ECCV.
[8] Joel Emer,et al. A method to estimate the energy consumption of deep neural networks , 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers.
[9] Wei Pan,et al. Towards Accurate Binary Convolutional Neural Network , 2017, NIPS.
[10] Raghuraman Krishnamoorthi,et al. Quantizing deep convolutional networks for efficient inference: A whitepaper , 2018, ArXiv.
[11] Swagath Venkataramani,et al. PACT: Parameterized Clipping Activation for Quantized Neural Networks , 2018, ArXiv.
[12] Bo Chen,et al. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[13] Eunhyeok Park,et al. Value-aware Quantization for Training and Inference of Neural Networks , 2018, ECCV.
[14] Avi Mendelson,et al. UNIQ: Uniform Noise Injection for the Quantization of Neural Networks , 2018, ArXiv.
[15] Dharmendra S. Modha,et al. Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference , 2018, ArXiv.
[16] Shuang Wu,et al. Training and Inference with Integers in Deep Neural Networks , 2018, ICLR.
[17] Jae-Joon Han,et al. Joint Training of Low-Precision Neural Network with Quantization Interval Parameters , 2018, ArXiv.
[18] Xiaoli Liu,et al. Highly Efficient 8-bit Low Precision Inference of Convolutional Neural Networks with IntelCaffe , 2018, ReQuEST@ASPLOS.
[19] Seungwon Lee,et al. Quantization for Rapid Deployment of Deep Neural Networks , 2018, ArXiv.
[20] Jae-Joon Han,et al. Learning to Quantize Deep Networks by Optimizing Quantization Intervals With Task Loss , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[21] Yoni Choukroun,et al. Low-bit Quantization of Neural Networks for Efficient Inference , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[22] Zhiru Zhang,et al. Improving Neural Network Quantization using Outlier Channel Splitting , 2019 .
[23] Alexander Goncharenko,et al. Fast Adjustable Threshold For Uniform Neural Network Quantization , 2018, IWANN.
[24] Alexander Finkelstein,et al. Same, Same But Different - Recovering Neural Network Quantization Error Through Weight Factorization , 2019, ICML.
[25] Avi Mendelson,et al. UNIQ , 2018, ACM Trans. Comput. Syst..