Bedot: Bit Efficient Dot Product for Deep Generative Models

Author:

Ho Nhut-Minh,Nguyen Duy-Thanh,Gustafson John L.,Wong Weng-Fai

Publisher

Springer Nature Switzerland

Reference42 articles.

1. Lin, Y., Li, Y., Liu, T., Xiao, T., Liu, T., Zhu, J.: Towards fully 8-bit integer inference for the transformer model. arXiv preprint arXiv:2009.08034 (2020)

2. Wang, P., et al.: QGAN: quantized generative adversarial networks. arXiv preprint arXiv:1901.08263 (2019)

3. Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. Adv. Neural. Inf. Process. Syst. 29, 2234–2242 (2016)

4. Wu, H., Judd, P., Zhang, X., Isaev, M., Micikevicius, P.: Integer quantization for deep learning inference: principles and empirical evaluation. arXiv preprint arXiv:2004.09602 (2020)

5. Kim, S., Kum, K.-I., Sung, W.: Fixed-point optimization utility for C and C++ based digital signal processing programs. IEEE Trans. Circuits Syst. II: Analog Digit. Signal Process. 45(11), 1455–1464 (1998)

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