Value-aware Quantization for Training and Inference of Neural Networks

Marcus, M.P., Marcinkiewicz, M.A., Santorini, B.: Building a large annotated corpus of English: The Penn Treebank. Computational linguistics 19(2), 313–330 (1993)

1993

Earlier work this paper cites.

Hong, S., Kim, H.: An integrated GPU power and performance model. In: International Symposium on Computer Architecture (ISCA). pp. 280–289 (2010)

2010

Earlier work this paper cites.

Marcel, S., Rodriguez, Y.: Torchvision the machine-vision package of torch. ACM Multimedia (2010)

2010

Earlier work this paper cites.

He, X., et al.: Practical lessons from predicting clicks on ads at facebook. In: International Workshop on Data Mining for Online Advertising (ADKDD). pp. 1–9. ACM (2014)

2014

Earlier work this paper cites.

Zaremba, W., Sutskever, I., Vinyals, O.: Recurrent neural network regularization. arXiv:1409.2329 (2014)

Original

2014

Earlier work this paper cites.

Chen, T., et al.: Training deep nets with sublinear memory cost. arXiv:1604.06174 (2016)

Original

2016

Earlier work this paper cites.

Hubara, I., et al.: Quantized neural networks: Training neural networks with low precision weights and activations. arXiv:1609.07061 (2016)

Original

2016

Earlier work this paper cites.

Inan, H., Khosravi, K., Socher, R.: Tying word vectors and word classifiers: A loss framework for language modeling. arXiv:1611.01462 (2016)

Original

2016

Earlier work this paper cites.

Konečnỳ, J., et al.: Federated learning: Strategies for improving communication efficiency. arXiv:1610.05492 (2016)

Original

2016

Earlier work this paper cites.

Miyashita, D., Lee, E.H., Murmann, B.: Convolutional neural networks using logarithmic data representation. arXiv:1603.01025 (2016)

Original

2016

Earlier work this paper cites.

Rastegari, M., et al.: Xnor-net: Imagenet classification using binary convolutional neural networks. the European Conference on Computer Vision (ECCV) pp. 525–542 (2016)

2016

Cited alongside, same era.

Zhou, S., et al.: Dorefa-net: Training low bitwidth convolutional neural networks with low bitwidth gradients. arXiv:1606.06160 (2016)

Original

2016

Cited alongside, same era.

Zhu, C., et al.: Trained ternary quantization. arXiv:1612.01064 (2016)

Original

2016

Cited alongside, same era.

Bakunas-Milanowski, D., et al.: Efficient algorithms for stream compaction on gpus. International Journal of Networking and Computing (IJNC) 7(2), 208–226 (2017)

2017

Cited alongside, same era.

Ginsburg, B., et al.: NVIDIA Mixed Precision Training on Volta GPUs. GPU Technology Conference (2017)

Park, E., Ahn, J., Yoo, S.: Weighted-entropy-based quantization for deep neural networks. Computer Vision and Pattern Recognition (CVPR) pp. 7197–7205 (2017)

2017

Later among the works it cites.

Paszke, A., et al.: Pytorch (2017)

2017

Later among the works it cites.

Press, O., Wolf, L.: Using the output embedding to improve language models. In: the European Chapter of the Association for Computational Linguistics (EACL). pp. 157–163 (2017)

2017

Later among the works it cites.

Umuroglu, Y., et al.: Finn: A framework for fast, scalable binarized neural network inference. In: ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA). pp. 65–74 (2017)

2017

Later among the works it cites.

Zhou, S., et al.: Balanced quantization: An effective and efficient approach to quantized neural networks. J. Comput. Sci. Technol. 32(4), 667–682 (2017)

2017