BiQGEMM: Matrix Multiplication with Lookup Table For Binary-Coding-based Quantized DNNs

Pearson Education India, 1974

A. V. Aho and J. E. Hopcroft, The design and analysis of computer algorithms · 1974

Earlier work this paper cites.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” nature

1986

Earlier work this paper cites.

J. Von Neumann, “First draft of a report on the EDVAC,” IEEE Annals of the History of Computing

1993

Earlier work this paper cites.

S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation

1997

Earlier work this paper cites.

C. Nvidia, “cuBLAS library,” NVIDIA Corporation, Santa Clara, California

2008

Earlier work this paper cites.

V. Volkov and J. W. Demmel, “Benchmarking GPUs to tune dense linear algebra,” in SC’08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing

2008

Earlier work this paper cites.

G. Guennebaud, B. Jacob, et al

2010

Earlier work this paper cites.

Elsevier, 2011

J. L. Hennessy and D. A. Patterson, Computer architecture: a quantitative approach · 2011

Earlier work this paper cites.

T. N. Sainath, B. Kingsbury, V. Sindhwani, E. Arisoy, and B. Ramabhadran, “Low-rank matrix factorization for deep neural network training with high-dimensional output targets,” in 2013 IEEE international conference on acoustics, speech and signal processing

2013

Earlier work this paper cites.

D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473

Original

2014

Earlier work this paper cites.

E. Wang, Q. Zhang, B. Shen, G. Zhang, X. Lu, Q. Wu, and Y. Wang, “Intel math kernel library,” in High-Performance Computing on the Intel® Xeon Phi™

2014

Earlier work this paper cites.

S. Chetlur, C. Woolley, P. Vandermersch, J. Cohen, J. Tran, B. Catanzaro, and E. Shelhamer, “cuDNN: Efficient primitives for deep learning,” arXiv preprint arXiv:1410.0759

Original

2014

Earlier work this paper cites.

S. Han, H. Mao, and W. J. Dally, “Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding,” arXiv preprint arXiv:1510.00149

Original

2015

Earlier work this paper cites.

Y. Li, Y. Liang, and A. Risteski, “Recovery guarantee of weighted low-rank approximation via alternating minimization,” in International Conference on Machine Learning

2016

Earlier work this paper cites.

M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, “XNOR-Net: Imagenet classification using binary convolutional neural networks,” in European conference on computer vision

2016

Earlier work this paper cites.

M. Courbariaux, I. Hubara, D. Soudry, R. El-Yaniv, and Y. Bengio, “Binarized neural networks: Training deep neural networks with weights and activations constrained to +1 or -1,” arXiv preprint arXiv:1602.02830

Original

2016

Earlier work this paper cites.

P. Rajpurkar, J. Zhang, K. Lopyrev, and P. Liang, “SQuAD: 100,000+ questions for machine comprehension of text,” arXiv preprint arXiv:1606.05250

Original

2016

Earlier work this paper cites.

Y. Cheng, D. Wang, P. Zhou, and T. Zhang, “A survey of model compression and acceleration for deep neural networks,” arXiv preprint arXiv:1710.09282

Original

2017

Earlier work this paper cites.

Y. Guo, A. Yao, H. Zhao, and Y. Chen, “Network sketching: Exploiting binary structure in deep CNNs,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

2017

Earlier work this paper cites.

A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems

2017

Cited alongside, same era.

K. Ahmed, N. S. Keskar, and R. Socher, “Weighted Transformer network for machine translation,” arXiv preprint arXiv:1711.02132

Original

2017

Cited alongside, same era.

D. He, Z. Wang, and J. Liu, “A survey to predict the trend of AI-able server evolution in the cloud,” IEEE Access

2018

Cited alongside, same era.

E. Eleftheriou, ““in-memory computing”: Accelerating ai applications,” in 2018 48th European Solid-State Device Research Conference (ESSDERC)

2018

Cited alongside, same era.

Z. Liu, M. Sun, T. Zhou, G. Huang, and T. Darrell, “Rethinking the value of network pruning,” arXiv preprint arXiv:1810.05270

Original

A. Reuther, P. Michaleas, M. Jones, V. Gadepally, S. Samsi, and J. Kepner, “Survey and benchmarking of machine learning accelerators,” arXiv preprint arXiv:1908.11348

Original

2019

Later among the works it cites.

Z. Zhou, X. Chen, E. Li, L. Zeng, K. Luo, and J. Zhang, “Edge intelligence: Paving the last mile of artificial intelligence with edge computing,” Proceedings of the IEEE

2019

Later among the works it cites.

D. Lee, S. J. Kwon, B. Kim, P. Kapoor, and G.-Y. Wei, “Network pruning for low-rank binary indexing,” arXiv preprint arXiv:1905.05686

Original

2019

Later among the works it cites.

A. Bhandare, V. Sripathi, D. Karkada, V. Menon, S. Choi, K. Datta, and V. Saletore, “Efficient 8-bit quantization of Transformer neural machine language translation model,” arXiv preprint arXiv:1906.00532

Original

2019

Later among the works it cites.