Commenced in January 2007
Paper Count: 31430
Fast Adjustable Threshold for Uniform Neural Network Quantization
Abstract:The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.3461980Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 383
 A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arXiv preprint arXiv:1704.04861, 2017.
 M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “Inverted residuals and linear bottlenecks: Mobile networks for classification, detection and segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2018).
 M. Tan, B. Chen, R. Pang, V. Vasudevan, and Q. V. Le, “Mnasnet: Platform-aware neural architecture search for mobile,” arXiv preprint arXiv:1807.11626, 2018.
 J. H. Lee, S. Ha, S. Choi, W. Lee, and S. Lee, “Quantization for rapid deployment of deep neural networks,” arXiv preprint arXiv:1810.05488, 2018. 5] B. Jacob, S. Kligys, B. Chen, M. Zhu, M. Tang, A. Howard, H. Adam, and D. Kalenichenko, “Quantization and training of neural networks for efficient integer-arithmetic only inference,” in Conference on Computer Vision and Pattern Recognition (CVPR 2018).
 G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” arXiv preprint arXiv:1503.02531, 2015.
 A. Mishra and D. Marr, “Apprentice: Using knowledge distillation techniques to improve low-precision network accuracy,” arXiv preprint arXiv:1711.05852, 2017.
 A. Mishra, E. Nurvitadhi, J. J. Cook, and D. Marr, “Wrpn: Wide reduced-precision networks,” arXiv preprint arXiv:1709.01134, 2017.
 https://developer.nvidia.com/tensorrt, NVIDIA TensorRT platform, 2018.
 M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mane, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viegas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, “Tensorflow: Largescale machine learning on heterogeneous distributed systems,” arXiv preprint arXiv:1603.04467, 2016.
 M. Courbariaux, Y. Bengio, and J. David, “Training deep neural networks with low precision multiplications,” in International Conference on Learning Representations (ICLR 2015).
 I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks,” in Advances in Neural Information Processing Systems (NIPS 2016), pp. 41074115.
 M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “Xnor-net: Imagenet classification using binary convolutional neural networks,” in European Conference on Computer Vision (ECCV 2016), Springer, pp. 525542.
 S. Zhou, Y. Wu, Z. Ni, X. Zhou, H. Wen, and Y. Zou, “Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients,” arXiv preprint arXiv:1606.06160, 2016.
 Y. Bengio, N. Leonard, and A. C. Courville, “Estimating or propagating gradients through stochastic neurons for conditional computation,” arXiv preprint arXiv:1308.3432, 2013.
 M. D. McDonnell, “Training wide residual networks for deployment using a single bit for each weight,” in International Conference on Learning Representations (ICLR 2018).
 S. Zhu, X. Dong, and H. Su, “Binary ensemble neural network: More bits per network or more networks per bit?” arXiv preprint arXiv:1806.07550, 2018.
 C. Baskin, N. Liss, Y. Chai, E. Zheltonozhskii, E. Schwartz, R. Giryes, A. Mendelson, and A. M. Bronstein, “Nice: Noise injection and clamping estimation for neural network quantization,” arXiv preprint arXiv:1810.00162, 2018.
 O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “Imagenet large scale visual recognition challenge,” arXiv preprint arXiv:1409.0575, 2014.
 S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (ICML 2015).
 D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” in International Conference on Learning Representations (ICLR 2015).
 T. Sheng, C. Feng, S. Zhuo, X. Zhang, L. Shen, and M. Aleksic, “A quantization-friendly separable convolution for mobilenets,” arXiv preprint arXiv:1803.08607, 2018.
 https://github.com/tensorflow/tensorflow/blob/ 61c6c84964b4aec80aeace187aab8cb2c3e55a72/tensorflow/lite/g3doc/ models.md, Image classification (Quantized Models).