LoWino: Towards Efficient Low-Precision Winograd Convolutions on Modern CPUs-Reference-Cited by-同舟云学术

LoWino: Towards Efficient Low-Precision Winograd Convolutions on Modern CPUs

Published:2021-08-09 Issue: Volume: Page:
ISSN:
Container-title:50th International Conference on Parallel Processing
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Author:

Li Guangli¹,Jia Zhen²,Feng Xiaobing¹,Wang Yida²

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.

1. Error Analysis and Improving the Accuracy of Winograd Convolution for Deep Neural Networks

2. DianNao family

3. Recent advances in efficient computation of deep convolutional neural networks

4. Low-bit Quantization of Neural Networks for Efficient Inference

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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