Efficient Residue Number System Based Winograd Convolution

Author:

Liu Zhi-Gang,Mattina Matthew

Publisher

Springer International Publishing

Reference14 articles.

1. Barabasz, B., Anderson, A., Soodhalter, K.M., Gregg, D.: Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks. arXiv e-prints arXiv:1803.10986 (2018)

2. Courbariaux, M., Bengio, Y.: Binarynet: Training deep neural networks with weights and activations constrained to +1 or –1. CoRR abs/1602.02830 (2016)

3. Dally, W.: Nips tutorial 2015 (2015). https://media.nips.cc/Conferences/2015/tutorialslides/Dally-NIPS-Tutorial-2015.pdf

4. Knuth, D.E.: The Art of Computer Programming, vol. 1: Fundamental Algorithms. §1.2.3: Sums and Products: Exercise 40, 3rd ed. (1997)

5. Knuth, D.E.: The Art of Computer Programming, vol. 2: Seminumerical Algorithms. Section 4.3.2, 3rd ed., pp. 286–291, exercise 4.6.2-3, p. 456. Addison-Wesley (2001)

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