LoWino: Towards Efficient Low-Precision Winograd Convolutions on Modern CPUs
Author:
Affiliation:
1. Institute of Computing Technology, Chinese Academy of Sciences and University of Chinese Academy of Sciences, China
2. Amazon, United States of America
Funder
Science Fund for Creative Research Groups of the National Natural Science Foundation of China
National Key R&D Program of China
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3472456.3472464
Reference47 articles.
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5. Matthieu Courbariaux Itay Hubara Daniel Soudry Ran El-Yaniv and Yoshua Bengio. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830(2016). Matthieu Courbariaux Itay Hubara Daniel Soudry Ran El-Yaniv and Yoshua Bengio. 2016. Binarized neural networks: Training deep neural networks with weights and activations constrained to+ 1 or-1. arXiv preprint arXiv:1602.02830(2016).
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