Training Deep Neural Networks with 8-bit Floating Point Numbers
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Daniel Brand | Kailash Gopalakrishnan | Jungwook Choi | Chia-Yu Chen | Naigang Wang | D. Brand | K. Gopalakrishnan | Chia-Yu Chen | Jungwook Choi | Naigang Wang
[1] Geoffrey Zweig,et al. The microsoft 2016 conversational speech recognition system , 2016, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[2] Shuchang Zhou,et al. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients , 2016, ArXiv.
[3] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[4] George Kurian,et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation , 2016, ArXiv.
[5] R. C. Whaley,et al. Reducing Floating Point Error in Dot Product Using the Superblock Family of Algorithms , 2008, SIAM J. Sci. Comput..
[6] Stuart C. Schwartz,et al. Best “ordering” for floating-point addition , 1988, TOMS.
[7] Pradeep Dubey,et al. Mixed Precision Training of Convolutional Neural Networks using Integer Operations , 2018, ICLR.
[8] Nicholas J. Higham,et al. The Accuracy of Floating Point Summation , 1993, SIAM J. Sci. Comput..
[9] Swagath Venkataramani,et al. PACT: Parameterized Clipping Activation for Quantized Neural Networks , 2018, ArXiv.
[10] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[11] Joel Silberman,et al. A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference , 2018, 2018 IEEE Symposium on VLSI Circuits.
[12] Swagath Venkataramani,et al. Exploiting approximate computing for deep learning acceleration , 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[13] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[14] A. Krizhevsky. Convolutional Deep Belief Networks on CIFAR-10 , 2010 .
[15] Xin Wang,et al. Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks , 2017, NIPS.
[16] Pritish Narayanan,et al. Deep Learning with Limited Numerical Precision , 2015, ICML.
[17] Hao Wu,et al. Mixed Precision Training , 2017, ICLR.
[18] Shuang Wu,et al. Training and Inference with Integers in Deep Neural Networks , 2018, ICLR.
[19] Bhuvana Ramabhadran,et al. Training variance and performance evaluation of neural networks in speech , 2017, 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[20] Suyog Gupta,et al. Model Accuracy and Runtime Tradeoff in Distributed Deep Learning: A Systematic Study , 2015, 2016 IEEE 16th International Conference on Data Mining (ICDM).
[21] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, ArXiv.