DREW: Efficient Winograd CNN Inference with Deep Reuse-Reference-Cited by-同舟云学术

DREW: Efficient Winograd CNN Inference with Deep Reuse

Published:2022-04-25 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the ACM Web Conference 2022
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Author:

Wu Ruofan¹,Zhang Feng¹,Guan Jiawei¹,Zheng Zhen²,Du Xiaoyong¹,Shen Xipeng³

Affiliation:

1. Renmin University of China, China

2. Alibaba Group, China

3. North Carolina State University, USA

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3485447.3511985

Reference64 articles.

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3. 2016. Intel(R) Math Kernel Library for Deep Neural Networks. https://github.com/oneapi-src/oneDNN. 2016. Intel(R) Math Kernel Library for Deep Neural Networks. https://github.com/oneapi-src/oneDNN.

4. 2020. CifarNet. http://places.csail.mit.edu/deepscene/small-projects/TRN-pytorch-pose/model_zoo/models/slim/nets/cifarnet.py. 2020. CifarNet. http://places.csail.mit.edu/deepscene/small-projects/TRN-pytorch-pose/model_zoo/models/slim/nets/cifarnet.py.

5. 2020. LIBXSMM. https://github.com/hfp/libxsmm. 2020. LIBXSMM. https://github.com/hfp/libxsmm.

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